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 "cells": [
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   "cell_type": "markdown",
   "id": "a641832b",
   "metadata": {},
   "source": [
    "(emcee_conversion)=\n",
    "# Converting emcee objects to InferenceData\n",
    "\n",
    "{class}`~arviz.InferenceData` is the central data format for ArviZ. `InferenceData` itself is just a container that maintains references to one or more {class}`xarray.Dataset`. \n",
    "\n",
    "Below are various ways to generate an `InferenceData` from emcee objects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "279a434d",
   "metadata": {},
   "source": [
    "```{seealso}\n",
    "\n",
    "- Conversion from Python, numpy or pandas objects\n",
    "- {ref}`xarray_for_arviz` for an overview of `InferenceData` and its role within ArviZ. \n",
    "- {ref}`schema` describes the structure of `InferenceData` objects and the assumptions made by ArviZ to ease your exploratory analysis of Bayesian models.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b702e7fd",
   "metadata": {},
   "source": [
    "We will start by importing the required packages and defining the model. The famous 8 school model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "87f7958f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import numpy as np\n",
    "import emcee"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9bdd7bbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "az.style.use(\"arviz-darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e9a05da",
   "metadata": {},
   "outputs": [],
   "source": [
    "J = 8\n",
    "y_obs = np.array([28.0, 8.0, -3.0, 7.0, -1.0, 1.0, 18.0, 12.0])\n",
    "sigma = np.array([15.0, 10.0, 16.0, 11.0, 9.0, 11.0, 10.0, 18.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1cd28960",
   "metadata": {},
   "outputs": [],
   "source": [
    "def log_prior_8school(theta):\n",
    "    mu, tau, eta = theta[0], theta[1], theta[2:]\n",
    "    # Half-cauchy prior, hwhm=25\n",
    "    if tau < 0:\n",
    "        return -np.inf\n",
    "    prior_tau = -np.log(tau**2 + 25**2)\n",
    "    prior_mu = -((mu / 10) ** 2)  # normal prior, loc=0, scale=10\n",
    "    prior_eta = -np.sum(eta**2)  # normal prior, loc=0, scale=1\n",
    "    return prior_mu + prior_tau + prior_eta\n",
    "\n",
    "\n",
    "def log_likelihood_8school(theta, y, s):\n",
    "    mu, tau, eta = theta[0], theta[1], theta[2:]\n",
    "    return -(((mu + tau * eta - y) / s) ** 2)\n",
    "\n",
    "\n",
    "def lnprob_8school(theta, y, s):\n",
    "    prior = log_prior_8school(theta)\n",
    "    like_vect = log_likelihood_8school(theta, y, s)\n",
    "    like = np.sum(like_vect)\n",
    "    return like + prior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cac78e4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "nwalkers = 40  # called chains in ArviZ\n",
    "ndim = J + 2\n",
    "draws = 1500\n",
    "pos = np.random.normal(size=(nwalkers, ndim))\n",
    "pos[:, 1] = np.absolute(pos[:, 1])\n",
    "sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob_8school, args=(y_obs, sigma))\n",
    "sampler.run_mcmc(pos, draws);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf6af8a4",
   "metadata": {},
   "source": [
    "## Manually set variable names\n",
    "This first example will show how to convert manually setting the variable names only, leaving everything else to ArviZ defaults."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "95f696a0",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 40, draw: 1500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 ... 1493 1494 1495 1496 1497 1498 1499\n",
       "Data variables:\n",
       "    mu       (chain, draw) float64 0.6982 0.7962 0.8433 ... 5.763 5.763 5.029\n",
       "    tau      (chain, draw) float64 0.6679 0.7259 0.8075 ... 2.051 2.051 3.239\n",
       "    eta0     (chain, draw) float64 0.08153 0.008519 0.007711 ... 0.4684 0.6057\n",
       "    eta1     (chain, draw) float64 -0.5837 -0.6358 -0.828 ... 1.431 1.431 1.608\n",
       "    eta2     (chain, draw) float64 0.104 -0.003427 0.08645 ... -1.056 -0.8344\n",
       "    eta3     (chain, draw) float64 0.8693 1.196 1.423 ... -1.621 -1.621 -0.8859\n",
       "    eta4     (chain, draw) float64 0.8211 1.27 1.324 ... -1.509 -1.509 -0.9923\n",
       "    eta5     (chain, draw) float64 0.04491 0.2302 0.1735 ... -0.8137 -0.5359\n",
       "    eta6     (chain, draw) float64 0.2983 0.1357 0.1385 ... -0.2085 0.0377\n",
       "    eta7     (chain, draw) float64 -0.5895 -0.5165 -0.6091 ... 0.1594 -0.03057\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:53.861857\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ac1e07fd-7b97-493b-aef4-ea2e4c85aa41' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ac1e07fd-7b97-493b-aef4-ea2e4c85aa41' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0da8b531-4a11-42dd-9b0c-4bb89299ac44' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0da8b531-4a11-42dd-9b0c-4bb89299ac44' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-7fba5b9b-aa64-4282-ad11-4cacc00207db' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fba5b9b-aa64-4282-ad11-4cacc00207db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b4d3598-b7a6-45d5-b6bf-2f793c869b6d' class='xr-var-data-in' type='checkbox'><label for='data-2b4d3598-b7a6-45d5-b6bf-2f793c869b6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-4de934f1-eb0f-43a0-9702-a34fa85391f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4de934f1-eb0f-43a0-9702-a34fa85391f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e906d0c-56ad-49e2-881b-eed8e8e66e4b' class='xr-var-data-in' type='checkbox'><label for='data-0e906d0c-56ad-49e2-881b-eed8e8e66e4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-846524b0-ae05-4ad2-b575-8a74fe3e57c3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-846524b0-ae05-4ad2-b575-8a74fe3e57c3' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6982 0.7962 ... 5.763 5.029</div><input id='attrs-30be37d9-f13a-4d90-a69a-a2fb69e550b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30be37d9-f13a-4d90-a69a-a2fb69e550b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fd73fd8-5a48-41ed-bba4-7c3d6591d199' class='xr-var-data-in' type='checkbox'><label for='data-2fd73fd8-5a48-41ed-bba4-7c3d6591d199' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.69824844,  0.79617544,  0.84330726, ...,  5.36725902,\n",
       "         5.36725902,  5.36725902],\n",
       "       [ 0.11248859,  0.11248859,  0.06015108, ...,  6.26414247,\n",
       "         7.48213444,  7.62666694],\n",
       "       [-0.93632231, -0.93632231, -0.88874485, ...,  2.07279112,\n",
       "         2.07279112,  2.07279112],\n",
       "       ...,\n",
       "       [-1.39513971, -0.6841303 , -0.22676674, ...,  8.03948904,\n",
       "         8.03948904,  8.03948904],\n",
       "       [-0.64258473, -0.63895969, -0.27810694, ...,  1.28046414,\n",
       "        -0.03145993, -0.03145993],\n",
       "       [-1.51237728, -1.51237728, -1.51237728, ...,  5.76290275,\n",
       "         5.76290275,  5.02928256]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6679 0.7259 ... 2.051 3.239</div><input id='attrs-044313af-cb48-442e-833c-7731779bace2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-044313af-cb48-442e-833c-7731779bace2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-783dd2f6-a1ea-482e-84d2-3ca0070e05b7' class='xr-var-data-in' type='checkbox'><label for='data-783dd2f6-a1ea-482e-84d2-3ca0070e05b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.66788403,  0.7259102 ,  0.80752206, ...,  0.03694788,\n",
       "         0.03694788,  0.03694788],\n",
       "       [ 0.3207948 ,  0.3207948 ,  0.41749554, ...,  9.79903119,\n",
       "         8.14156878,  7.60103129],\n",
       "       [ 1.04038851,  1.04038851,  1.06008197, ..., 17.27429606,\n",
       "        17.27429606, 17.27429606],\n",
       "       ...,\n",
       "       [ 1.33245704,  1.06653867,  1.02533509, ...,  0.22325512,\n",
       "         0.22325512,  0.22325512],\n",
       "       [ 0.53704382,  0.49084296,  0.82428818, ...,  0.97799322,\n",
       "         0.71121769,  0.71121769],\n",
       "       [ 1.03024648,  1.03024648,  1.03024648, ...,  2.05085976,\n",
       "         2.05085976,  3.23924793]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08153 0.008519 ... 0.4684 0.6057</div><input id='attrs-ea351b16-aac1-4019-a20f-0ccbc39e8d3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ea351b16-aac1-4019-a20f-0ccbc39e8d3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f54b039-7176-4360-8a13-34212cd461bb' class='xr-var-data-in' type='checkbox'><label for='data-2f54b039-7176-4360-8a13-34212cd461bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.08152575,  0.00851919,  0.00771088, ...,  0.01908609,\n",
       "         0.01908609,  0.01908609],\n",
       "       [ 0.51822161,  0.51822161,  0.52702754, ...,  0.77482336,\n",
       "         1.00139478,  1.01300919],\n",
       "       [ 1.96407299,  1.96407299,  1.82762146, ...,  0.81201598,\n",
       "         0.81201598,  0.81201598],\n",
       "       ...,\n",
       "       [ 0.54905919, -0.30758132, -0.15850033, ...,  0.71909529,\n",
       "         0.71909529,  0.71909529],\n",
       "       [-1.49816062, -1.60204414, -1.26912917, ..., -0.63261196,\n",
       "        -0.78396615, -0.78396615],\n",
       "       [-0.40230539, -0.40230539, -0.40230539, ...,  0.46842652,\n",
       "         0.46842652,  0.60565958]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5837 -0.6358 ... 1.431 1.608</div><input id='attrs-4b54d6be-8faa-4be6-9a13-c95ece9e11d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b54d6be-8faa-4be6-9a13-c95ece9e11d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da40eb72-3041-44a9-9de5-416ed328bed9' class='xr-var-data-in' type='checkbox'><label for='data-da40eb72-3041-44a9-9de5-416ed328bed9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.58367651, -0.63579839, -0.82795929, ..., -0.33392221,\n",
       "        -0.33392221, -0.33392221],\n",
       "       [-0.27190443, -0.27190443, -0.82228464, ...,  0.36996327,\n",
       "         0.63805827,  0.42878518],\n",
       "       [ 0.7449581 ,  0.7449581 ,  0.72617796, ...,  0.41748271,\n",
       "         0.41748271,  0.41748271],\n",
       "       ...,\n",
       "       [ 1.02197852,  0.96290899,  0.60247571, ...,  1.04108037,\n",
       "         1.04108037,  1.04108037],\n",
       "       [ 0.34551004,  0.29163914, -0.10763165, ..., -0.32320034,\n",
       "        -0.38804561, -0.38804561],\n",
       "       [-0.34432128, -0.34432128, -0.34432128, ...,  1.43086564,\n",
       "         1.43086564,  1.60761732]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.104 -0.003427 ... -1.056 -0.8344</div><input id='attrs-e6935084-2b22-4165-b9a7-2ac618410257' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6935084-2b22-4165-b9a7-2ac618410257' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-235372a0-24ca-4f80-a29b-6e702c832be6' class='xr-var-data-in' type='checkbox'><label for='data-235372a0-24ca-4f80-a29b-6e702c832be6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.10398099, -0.00342714,  0.08644913, ...,  0.3755985 ,\n",
       "         0.3755985 ,  0.3755985 ],\n",
       "       [ 0.7464532 ,  0.7464532 ,  0.37312608, ..., -0.70063911,\n",
       "        -0.71519927, -0.81143714],\n",
       "       [-0.32280405, -0.32280405, -0.31178929, ..., -0.51019   ,\n",
       "        -0.51019   , -0.51019   ],\n",
       "       ...,\n",
       "       [-0.45466237, -0.05838037,  0.03900794, ..., -0.21620162,\n",
       "        -0.21620162, -0.21620162],\n",
       "       [-2.04671119, -2.2202022 , -1.19514007, ...,  1.33819298,\n",
       "         1.50462748,  1.50462748],\n",
       "       [-0.9050526 , -0.9050526 , -0.9050526 , ..., -1.05638496,\n",
       "        -1.05638496, -0.83442841]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8693 1.196 ... -1.621 -0.8859</div><input id='attrs-95813eaf-6586-411a-8224-a6a054e37362' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95813eaf-6586-411a-8224-a6a054e37362' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f8745dd7-a3b8-4454-a4dd-ae52ea73d293' class='xr-var-data-in' type='checkbox'><label for='data-f8745dd7-a3b8-4454-a4dd-ae52ea73d293' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.8693095 ,  1.19621798,  1.42303564, ..., -0.79560144,\n",
       "        -0.79560144, -0.79560144],\n",
       "       [-1.08612533, -1.08612533, -0.54359142, ...,  0.90278086,\n",
       "         1.15766589,  0.7120672 ],\n",
       "       [ 1.39504785,  1.39504785,  1.31201871, ...,  0.22067313,\n",
       "         0.22067313,  0.22067313],\n",
       "       ...,\n",
       "       [-1.87925062, -0.21780186, -0.11415233, ...,  0.83981125,\n",
       "         0.83981125,  0.83981125],\n",
       "       [ 1.20783543,  1.33222884,  1.14406311, ..., -0.01284912,\n",
       "        -0.08905337, -0.08905337],\n",
       "       [ 0.37776041,  0.37776041,  0.37776041, ..., -1.62086036,\n",
       "        -1.62086036, -0.88594592]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8211 1.27 ... -1.509 -0.9923</div><input id='attrs-75e5fb71-3bea-4bcc-8048-4034b36afae8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75e5fb71-3bea-4bcc-8048-4034b36afae8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed8fd07a-4419-484d-a555-6ddda7cf38bd' class='xr-var-data-in' type='checkbox'><label for='data-ed8fd07a-4419-484d-a555-6ddda7cf38bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.82105387,  1.26961709,  1.32416423, ..., -0.2459569 ,\n",
       "        -0.2459569 , -0.2459569 ],\n",
       "       [-1.86207063, -1.86207063, -1.49599093, ...,  0.06620745,\n",
       "         0.14848399, -0.07355801],\n",
       "       [ 1.28605108,  1.28605108,  1.25958692, ..., -0.60792979,\n",
       "        -0.60792979, -0.60792979],\n",
       "       ...,\n",
       "       [-1.18420667, -0.71923738, -0.27185226, ...,  0.13602594,\n",
       "         0.13602594,  0.13602594],\n",
       "       [ 0.0369629 ,  0.10294485,  0.43936142, ..., -0.62330586,\n",
       "        -0.63818442, -0.63818442],\n",
       "       [-0.12814047, -0.12814047, -0.12814047, ..., -1.50864077,\n",
       "        -1.50864077, -0.99230636]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta5</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.04491 0.2302 ... -0.8137 -0.5359</div><input id='attrs-11867f32-7a60-42cd-b24f-a34034833c96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11867f32-7a60-42cd-b24f-a34034833c96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64a47bb0-6baa-432d-b9a3-8584724dceec' class='xr-var-data-in' type='checkbox'><label for='data-64a47bb0-6baa-432d-b9a3-8584724dceec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.04490954,  0.2302036 ,  0.17352078, ...,  0.30174842,\n",
       "         0.30174842,  0.30174842],\n",
       "       [-1.06344488, -1.06344488, -1.06039012, ..., -0.78676276,\n",
       "        -1.00580536, -0.8994664 ],\n",
       "       [-0.13577697, -0.13577697, -0.14314371, ...,  0.17122446,\n",
       "         0.17122446,  0.17122446],\n",
       "       ...,\n",
       "       [ 1.72646293,  0.49537014,  0.32919773, ..., -0.38252575,\n",
       "        -0.38252575, -0.38252575],\n",
       "       [-0.42622842, -0.50664213,  0.04164551, ..., -0.22083327,\n",
       "        -0.23227815, -0.23227815],\n",
       "       [ 0.00442761,  0.00442761,  0.00442761, ..., -0.81374118,\n",
       "        -0.81374118, -0.53590256]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta6</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2983 0.1357 ... -0.2085 0.0377</div><input id='attrs-957496c7-cb1b-4bce-9cc8-e4860cac84f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-957496c7-cb1b-4bce-9cc8-e4860cac84f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a33afca6-d9d2-460e-be4d-8e1714a4d950' class='xr-var-data-in' type='checkbox'><label for='data-a33afca6-d9d2-460e-be4d-8e1714a4d950' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.29826293,  0.13570644,  0.1384578 , ...,  0.76155516,\n",
       "         0.76155516,  0.76155516],\n",
       "       [ 1.27061036,  1.27061036,  0.71765789, ...,  0.42466312,\n",
       "         0.15725957,  0.05649381],\n",
       "       [-0.45382172, -0.45382172, -0.40167441, ...,  0.42766215,\n",
       "         0.42766215,  0.42766215],\n",
       "       ...,\n",
       "       [ 0.24977824, -0.1289273 ,  0.09393249, ..., -0.21744701,\n",
       "        -0.21744701, -0.21744701],\n",
       "       [ 0.19870891,  0.22729667,  0.85836095, ..., -0.15337504,\n",
       "        -0.04459114, -0.04459114],\n",
       "       [-0.1805794 , -0.1805794 , -0.1805794 , ..., -0.20851802,\n",
       "        -0.20851802,  0.0377028 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta7</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.5895 -0.5165 ... 0.1594 -0.03057</div><input id='attrs-14f2bdd0-0e77-420d-8818-804a6930e9cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14f2bdd0-0e77-420d-8818-804a6930e9cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe0b9774-fb00-4b52-86d3-f5856b66cc3b' class='xr-var-data-in' type='checkbox'><label for='data-fe0b9774-fb00-4b52-86d3-f5856b66cc3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.58949077, -0.51653613, -0.60913273, ..., -0.28262954,\n",
       "        -0.28262954, -0.28262954],\n",
       "       [-1.02587602, -1.02587602, -1.18220311, ..., -0.70635198,\n",
       "        -1.26786956, -0.87499378],\n",
       "       [ 0.43803847,  0.43803847,  0.39577369, ..., -0.87502477,\n",
       "        -0.87502477, -0.87502477],\n",
       "       ...,\n",
       "       [ 0.01571683,  0.7008197 ,  0.67271331, ...,  0.51383304,\n",
       "         0.51383304,  0.51383304],\n",
       "       [-1.20391145, -1.37010801, -1.05653758, ..., -0.8976637 ,\n",
       "        -0.91648287, -0.91648287],\n",
       "       [ 1.80343844,  1.80343844,  1.80343844, ...,  0.15942518,\n",
       "         0.15942518, -0.03056562]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f1bdbd3-9b9c-4fc2-98a3-8f2ab5a570b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f1bdbd3-9b9c-4fc2-98a3-8f2ab5a570b8' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:53.861857</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_statsb9b73893-349f-46e5-bc50-64d438b2bc71\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "<defs>\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       ".xr-section-summary-in:checked + label:before {\n",
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       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
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       ".xr-array-wrap {\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
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       "\n",
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       "\n",
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       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
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       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
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       "\n",
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       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "\n",
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       "  display: block;\n",
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       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
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       "\n",
       ".xr-attrs,\n",
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       ".xr-var-data {\n",
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       "\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 40, draw: 1500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 ... 1493 1494 1495 1496 1497 1498 1499\n",
       "Data variables:\n",
       "    lp       (chain, draw) float64 -16.3 -17.83 -18.92 ... -20.11 -20.11 -16.68\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:53.851570\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4d99e64f-5997-47d5-af42-c1e66bbf5feb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d99e64f-5997-47d5-af42-c1e66bbf5feb' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4fe7c8ee-19c7-4f04-b18e-a3b598b650b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4fe7c8ee-19c7-4f04-b18e-a3b598b650b4' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-d0f5a572-c780-40f0-bb70-6de45c985362' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d0f5a572-c780-40f0-bb70-6de45c985362' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d99df09c-63fa-4486-aaa6-78a41e1a7649' class='xr-var-data-in' type='checkbox'><label for='data-d99df09c-63fa-4486-aaa6-78a41e1a7649' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-9d1f30ff-dc4b-4c53-9b4d-4f05bb70693b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9d1f30ff-dc4b-4c53-9b4d-4f05bb70693b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d899b43a-6c0e-4049-80eb-67ceee6ac524' class='xr-var-data-in' type='checkbox'><label for='data-d899b43a-6c0e-4049-80eb-67ceee6ac524' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d24f11d-0491-4ef6-aa39-7bf709453a0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9d24f11d-0491-4ef6-aa39-7bf709453a0a' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-16.3 -17.83 ... -20.11 -16.68</div><input id='attrs-e6e299ab-b6fd-4b88-ab1e-8865792f00b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6e299ab-b6fd-4b88-ab1e-8865792f00b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd920b99-ff58-422a-9a0d-af32a6cff36d' class='xr-var-data-in' type='checkbox'><label for='data-cd920b99-ff58-422a-9a0d-af32a6cff36d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-16.29703737, -17.82792262, -18.92214387, ..., -13.44930902,\n",
       "        -13.44930902, -13.44930902],\n",
       "       [-23.84232789, -23.84232789, -21.27484126, ..., -13.70035183,\n",
       "        -17.53674728, -14.41060779],\n",
       "       [-23.52855761, -23.52855761, -22.5780883 , ..., -13.55239988,\n",
       "        -13.55239988, -13.55239988],\n",
       "       ...,\n",
       "       [-25.50638851, -17.64822888, -15.83134714, ..., -14.60346133,\n",
       "        -14.60346133, -14.60346133],\n",
       "       [-25.11716475, -26.98472303, -21.36045048, ..., -17.52355592,\n",
       "        -19.35557577, -19.35557577],\n",
       "       [-20.86324354, -20.86324354, -20.86324354, ..., -20.1093519 ,\n",
       "        -20.1093519 , -16.6765458 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-838dc601-941e-4b82-ac6e-ccfea14e4455' class='xr-section-summary-in' type='checkbox'  checked><label for='section-838dc601-941e-4b82-ac6e-ccfea14e4455' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:53.851570</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
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       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (arg_0_dim_0: 8, arg_1_dim_0: 8)\n",
       "Coordinates:\n",
       "  * arg_0_dim_0  (arg_0_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "  * arg_1_dim_0  (arg_1_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    arg_0        (arg_0_dim_0) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
       "    arg_1        (arg_1_dim_0) float64 15.0 10.0 16.0 11.0 9.0 11.0 10.0 18.0\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:53.853598\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-88f8aabf-55c4-4c10-9632-a8c5fcaa4dc8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-88f8aabf-55c4-4c10-9632-a8c5fcaa4dc8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>arg_0_dim_0</span>: 8</li><li><span class='xr-has-index'>arg_1_dim_0</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5dcab18b-e985-4db0-9b88-61d3e82d9818' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5dcab18b-e985-4db0-9b88-61d3e82d9818' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>arg_0_dim_0</span></div><div class='xr-var-dims'>(arg_0_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-7423df36-1036-48b3-b7e4-442f2f4615a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7423df36-1036-48b3-b7e4-442f2f4615a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fa0d8fe-e86b-44b6-b2a0-72b61f9dfc46' class='xr-var-data-in' type='checkbox'><label for='data-5fa0d8fe-e86b-44b6-b2a0-72b61f9dfc46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>arg_1_dim_0</span></div><div class='xr-var-dims'>(arg_1_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-9fd58385-8312-449f-8950-d2e64922aabd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fd58385-8312-449f-8950-d2e64922aabd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80d73e8b-8c17-43f7-9311-dc83af972d1e' class='xr-var-data-in' type='checkbox'><label for='data-80d73e8b-8c17-43f7-9311-dc83af972d1e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-706aa8c0-859f-47a3-b434-ec14011242bb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-706aa8c0-859f-47a3-b434-ec14011242bb' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>arg_0</span></div><div class='xr-var-dims'>(arg_0_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0</div><input id='attrs-2626ec10-998d-4f79-9fbe-056cc3ec4303' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2626ec10-998d-4f79-9fbe-056cc3ec4303' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-104d8608-f1ac-4e83-99a5-06d6bf940abb' class='xr-var-data-in' type='checkbox'><label for='data-104d8608-f1ac-4e83-99a5-06d6bf940abb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([28.,  8., -3.,  7., -1.,  1., 18., 12.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>arg_1</span></div><div class='xr-var-dims'>(arg_1_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 10.0 16.0 ... 11.0 10.0 18.0</div><input id='attrs-c9321ddd-4b2e-459e-b31d-18372a1184f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c9321ddd-4b2e-459e-b31d-18372a1184f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65a062de-94de-43e0-a5b5-a7a7415364b0' class='xr-var-data-in' type='checkbox'><label for='data-65a062de-94de-43e0-a5b5-a7a7415364b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15., 10., 16., 11.,  9., 11., 10., 18.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-532c049a-666b-4206-9bfe-828ce97f0b91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-532c049a-666b-4206-9bfe-828ce97f0b91' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:53.853598</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define variable names, it cannot be inferred from emcee\n",
    "var_names = [\"mu\", \"tau\"] + [\"eta{}\".format(i) for i in range(J)]\n",
    "idata1 = az.from_emcee(sampler, var_names=var_names)\n",
    "idata1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "421a4564",
   "metadata": {},
   "source": [
    "ArviZ has stored the posterior variables with the provided names as expected, but it has also included other useful information in the `InferenceData` object. The log probability of each sample is stored in the `sample_stats` group under the name `lp` and all the arguments passed to the sampler as `args` have been saved in the `observed_data` group."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd01d5ae",
   "metadata": {},
   "source": [
    "It can also be useful to perform a burn in cut to the MCMC samples (see :meth:`arviz.InferenceData.sel` for more details)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f211e3e",
   "metadata": {},
   "outputs": [
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 40, draw: 1400)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
       "  * draw     (draw) int64 100 101 102 103 104 105 ... 1495 1496 1497 1498 1499\n",
       "Data variables:\n",
       "    mu       (chain, draw) float64 6.588 6.588 6.588 7.358 ... 5.763 5.763 5.029\n",
       "    tau      (chain, draw) float64 1.122 1.122 1.122 1.328 ... 2.051 2.051 3.239\n",
       "    eta0     (chain, draw) float64 -0.4995 -0.4995 -0.4995 ... 0.4684 0.6057\n",
       "    eta1     (chain, draw) float64 0.2038 0.2038 0.2038 ... 1.431 1.431 1.608\n",
       "    eta2     (chain, draw) float64 0.1563 0.1563 0.1563 ... -1.056 -0.8344\n",
       "    eta3     (chain, draw) float64 0.04793 0.04793 0.04793 ... -1.621 -0.8859\n",
       "    eta4     (chain, draw) float64 -1.467 -1.467 -1.467 ... -1.509 -0.9923\n",
       "    eta5     (chain, draw) float64 0.4489 0.4489 0.4489 ... -0.8137 -0.5359\n",
       "    eta6     (chain, draw) float64 0.1747 0.1747 0.1747 ... -0.2085 0.0377\n",
       "    eta7     (chain, draw) float64 0.4413 0.4413 0.4413 ... 0.1594 -0.03057\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:53.861857\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-995b5901-3def-46f2-9de7-3dfcce383bb5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-995b5901-3def-46f2-9de7-3dfcce383bb5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2c64bc56-bd62-478a-833f-8754bca99748' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c64bc56-bd62-478a-833f-8754bca99748' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-502507ba-902a-4c69-85fc-eb381e2a96e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-502507ba-902a-4c69-85fc-eb381e2a96e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bcf3af2-59a0-4e54-a334-0f2f3ec1e8d5' class='xr-var-data-in' type='checkbox'><label for='data-6bcf3af2-59a0-4e54-a334-0f2f3ec1e8d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100 101 102 103 ... 1497 1498 1499</div><input id='attrs-fd78d82a-ee3a-4e24-8523-30f891327a3b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fd78d82a-ee3a-4e24-8523-30f891327a3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb3b0530-af62-4174-9bfb-ad82ce3d90f8' class='xr-var-data-in' type='checkbox'><label for='data-fb3b0530-af62-4174-9bfb-ad82ce3d90f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 100,  101,  102, ..., 1497, 1498, 1499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ff8465c-05d5-491f-9934-1ae7e8aa8f3e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ff8465c-05d5-491f-9934-1ae7e8aa8f3e' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.588 6.588 6.588 ... 5.763 5.029</div><input id='attrs-a7dfe74b-8380-472e-b0fb-e4f9e2b3b1ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7dfe74b-8380-472e-b0fb-e4f9e2b3b1ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58de5e43-74ef-4275-b832-f44c3df25c97' class='xr-var-data-in' type='checkbox'><label for='data-58de5e43-74ef-4275-b832-f44c3df25c97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 6.5878517 ,  6.5878517 ,  6.5878517 , ...,  5.36725902,\n",
       "         5.36725902,  5.36725902],\n",
       "       [ 8.78769258,  7.82575182,  7.82575182, ...,  6.26414247,\n",
       "         7.48213444,  7.62666694],\n",
       "       [ 5.52583051,  5.52583051,  5.12719649, ...,  2.07279112,\n",
       "         2.07279112,  2.07279112],\n",
       "       ...,\n",
       "       [ 4.61482935,  4.47024796,  4.81591608, ...,  8.03948904,\n",
       "         8.03948904,  8.03948904],\n",
       "       [ 0.92422831,  0.92422831,  0.92422831, ...,  1.28046414,\n",
       "        -0.03145993, -0.03145993],\n",
       "       [-0.53976633, -0.53976633, -0.65763254, ...,  5.76290275,\n",
       "         5.76290275,  5.02928256]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.122 1.122 1.122 ... 2.051 3.239</div><input id='attrs-a42dce1a-f221-4c2a-821c-bdca4fcb1d5d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a42dce1a-f221-4c2a-821c-bdca4fcb1d5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25d68500-c3af-4151-9066-1482295028c1' class='xr-var-data-in' type='checkbox'><label for='data-25d68500-c3af-4151-9066-1482295028c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 1.1223702 ,  1.1223702 ,  1.1223702 , ...,  0.03694788,\n",
       "         0.03694788,  0.03694788],\n",
       "       [ 1.74417772,  1.62659584,  1.62659584, ...,  9.79903119,\n",
       "         8.14156878,  7.60103129],\n",
       "       [ 2.08514909,  2.08514909,  2.22684325, ..., 17.27429606,\n",
       "        17.27429606, 17.27429606],\n",
       "       ...,\n",
       "       [ 3.61158678,  4.11582   ,  4.32372071, ...,  0.22325512,\n",
       "         0.22325512,  0.22325512],\n",
       "       [ 0.0385638 ,  0.0385638 ,  0.0385638 , ...,  0.97799322,\n",
       "         0.71121769,  0.71121769],\n",
       "       [ 1.81281716,  1.81281716,  1.81384961, ...,  2.05085976,\n",
       "         2.05085976,  3.23924793]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.4995 -0.4995 ... 0.4684 0.6057</div><input id='attrs-0e2bf451-9a08-42ae-b3be-b31ff533d498' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e2bf451-9a08-42ae-b3be-b31ff533d498' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f13dd466-5dc9-4c17-8eac-93d21fba3823' class='xr-var-data-in' type='checkbox'><label for='data-f13dd466-5dc9-4c17-8eac-93d21fba3823' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-0.49953247, -0.49953247, -0.49953247, ...,  0.01908609,\n",
       "         0.01908609,  0.01908609],\n",
       "       [ 0.51180636,  0.52862043,  0.52862043, ...,  0.77482336,\n",
       "         1.00139478,  1.01300919],\n",
       "       [ 1.08447267,  1.08447267,  1.39778419, ...,  0.81201598,\n",
       "         0.81201598,  0.81201598],\n",
       "       ...,\n",
       "       [ 0.42927276,  0.52216828,  0.38516008, ...,  0.71909529,\n",
       "         0.71909529,  0.71909529],\n",
       "       [-1.07085722, -1.07085722, -1.07085722, ..., -0.63261196,\n",
       "        -0.78396615, -0.78396615],\n",
       "       [ 0.43718476,  0.43718476,  0.43870856, ...,  0.46842652,\n",
       "         0.46842652,  0.60565958]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.2038 0.2038 ... 1.431 1.608</div><input id='attrs-661b538d-910e-49b5-aecb-192022ce3165' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-661b538d-910e-49b5-aecb-192022ce3165' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7590a513-a9a7-422b-a956-d7eb76d0ed4c' class='xr-var-data-in' type='checkbox'><label for='data-7590a513-a9a7-422b-a956-d7eb76d0ed4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.20375509,  0.20375509,  0.20375509, ..., -0.33392221,\n",
       "        -0.33392221, -0.33392221],\n",
       "       [-0.47897209, -0.39267028, -0.39267028, ...,  0.36996327,\n",
       "         0.63805827,  0.42878518],\n",
       "       [-1.13749575, -1.13749575, -1.43436789, ...,  0.41748271,\n",
       "         0.41748271,  0.41748271],\n",
       "       ...,\n",
       "       [ 0.72401666,  0.94771601,  0.95452915, ...,  1.04108037,\n",
       "         1.04108037,  1.04108037],\n",
       "       [-0.21646498, -0.21646498, -0.21646498, ..., -0.32320034,\n",
       "        -0.38804561, -0.38804561],\n",
       "       [ 0.05308387,  0.05308387,  0.06135379, ...,  1.43086564,\n",
       "         1.43086564,  1.60761732]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta2</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1563 0.1563 ... -1.056 -0.8344</div><input id='attrs-32969d19-4e1b-4af8-8174-5efdbf47601e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32969d19-4e1b-4af8-8174-5efdbf47601e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14890e6b-1435-4587-bf18-3ca3a7f5cef0' class='xr-var-data-in' type='checkbox'><label for='data-14890e6b-1435-4587-bf18-3ca3a7f5cef0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.15629831,  0.15629831,  0.15629831, ...,  0.3755985 ,\n",
       "         0.3755985 ,  0.3755985 ],\n",
       "       [ 0.07646884,  0.22982474,  0.22982474, ..., -0.70063911,\n",
       "        -0.71519927, -0.81143714],\n",
       "       [ 0.95026631,  0.95026631,  1.5276213 , ..., -0.51019   ,\n",
       "        -0.51019   , -0.51019   ],\n",
       "       ...,\n",
       "       [-0.12921561, -0.14536789, -0.20631732, ..., -0.21620162,\n",
       "        -0.21620162, -0.21620162],\n",
       "       [-0.18553106, -0.18553106, -0.18553106, ...,  1.33819298,\n",
       "         1.50462748,  1.50462748],\n",
       "       [ 0.36181655,  0.36181655,  0.3747941 , ..., -1.05638496,\n",
       "        -1.05638496, -0.83442841]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta3</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.04793 0.04793 ... -1.621 -0.8859</div><input id='attrs-5538d159-bf4a-41d4-8518-2783bc5c53c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5538d159-bf4a-41d4-8518-2783bc5c53c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42d59c7a-6250-4761-9d69-aa5aa4074341' class='xr-var-data-in' type='checkbox'><label for='data-42d59c7a-6250-4761-9d69-aa5aa4074341' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.0479263 ,  0.0479263 ,  0.0479263 , ..., -0.79560144,\n",
       "        -0.79560144, -0.79560144],\n",
       "       [-0.11674534,  0.009035  ,  0.009035  , ...,  0.90278086,\n",
       "         1.15766589,  0.7120672 ],\n",
       "       [-0.21965611, -0.21965611, -0.42872324, ...,  0.22067313,\n",
       "         0.22067313,  0.22067313],\n",
       "       ...,\n",
       "       [-0.16579112, -0.13190644, -0.09796613, ...,  0.83981125,\n",
       "         0.83981125,  0.83981125],\n",
       "       [-1.02524753, -1.02524753, -1.02524753, ..., -0.01284912,\n",
       "        -0.08905337, -0.08905337],\n",
       "       [-0.23527656, -0.23527656, -0.24382442, ..., -1.62086036,\n",
       "        -1.62086036, -0.88594592]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta4</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.467 -1.467 ... -1.509 -0.9923</div><input id='attrs-563eedaa-7db9-47bb-9efa-c3ce34e9275c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-563eedaa-7db9-47bb-9efa-c3ce34e9275c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8e10c7c-5707-4356-84ac-e5777549e26b' class='xr-var-data-in' type='checkbox'><label for='data-a8e10c7c-5707-4356-84ac-e5777549e26b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-1.46657444, -1.46657444, -1.46657444, ..., -0.2459569 ,\n",
       "        -0.2459569 , -0.2459569 ],\n",
       "       [ 0.03246754, -0.0400725 , -0.0400725 , ...,  0.06620745,\n",
       "         0.14848399, -0.07355801],\n",
       "       [-0.2602346 , -0.2602346 ,  0.36360168, ..., -0.60792979,\n",
       "        -0.60792979, -0.60792979],\n",
       "       ...,\n",
       "       [-0.42345642, -0.38157343, -0.52230329, ...,  0.13602594,\n",
       "         0.13602594,  0.13602594],\n",
       "       [ 0.45520304,  0.45520304,  0.45520304, ..., -0.62330586,\n",
       "        -0.63818442, -0.63818442],\n",
       "       [-0.1738624 , -0.1738624 , -0.14768729, ..., -1.50864077,\n",
       "        -1.50864077, -0.99230636]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta5</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4489 0.4489 ... -0.8137 -0.5359</div><input id='attrs-4063fc4c-c6b5-4cda-92a4-62687aa6a47b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4063fc4c-c6b5-4cda-92a4-62687aa6a47b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-249fd552-19d8-4711-9dec-4f139e849376' class='xr-var-data-in' type='checkbox'><label for='data-249fd552-19d8-4711-9dec-4f139e849376' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.44890576,  0.44890576,  0.44890576, ...,  0.30174842,\n",
       "         0.30174842,  0.30174842],\n",
       "       [ 1.07737541,  0.92208136,  0.92208136, ..., -0.78676276,\n",
       "        -1.00580536, -0.8994664 ],\n",
       "       [ 0.11609463,  0.11609463,  0.33724537, ...,  0.17122446,\n",
       "         0.17122446,  0.17122446],\n",
       "       ...,\n",
       "       [ 0.64240369,  0.79305623,  0.90431255, ..., -0.38252575,\n",
       "        -0.38252575, -0.38252575],\n",
       "       [-0.41489435, -0.41489435, -0.41489435, ..., -0.22083327,\n",
       "        -0.23227815, -0.23227815],\n",
       "       [ 0.85061344,  0.85061344,  0.87173743, ..., -0.81374118,\n",
       "        -0.81374118, -0.53590256]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta6</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1747 0.1747 ... -0.2085 0.0377</div><input id='attrs-54ca1b4d-e3d3-46f6-8f7d-9c0607386a42' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54ca1b4d-e3d3-46f6-8f7d-9c0607386a42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d16500aa-0cfa-4552-8e86-b1ba9b791525' class='xr-var-data-in' type='checkbox'><label for='data-d16500aa-0cfa-4552-8e86-b1ba9b791525' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.17474467,  0.17474467,  0.17474467, ...,  0.76155516,\n",
       "         0.76155516,  0.76155516],\n",
       "       [ 0.0429738 ,  0.17987497,  0.17987497, ...,  0.42466312,\n",
       "         0.15725957,  0.05649381],\n",
       "       [ 0.73140249,  0.73140249,  1.19271605, ...,  0.42766215,\n",
       "         0.42766215,  0.42766215],\n",
       "       ...,\n",
       "       [-0.99158183, -0.98537934, -1.20985459, ..., -0.21744701,\n",
       "        -0.21744701, -0.21744701],\n",
       "       [ 0.49162222,  0.49162222,  0.49162222, ..., -0.15337504,\n",
       "        -0.04459114, -0.04459114],\n",
       "       [ 0.00923693,  0.00923693,  0.01536582, ..., -0.20851802,\n",
       "        -0.20851802,  0.0377028 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta7</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4413 0.4413 ... 0.1594 -0.03057</div><input id='attrs-5ed1cfc7-1fd1-4bcd-a8a7-d0c2fefbd5ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ed1cfc7-1fd1-4bcd-a8a7-d0c2fefbd5ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-159841ef-80f1-49e8-80a4-972e99c965f7' class='xr-var-data-in' type='checkbox'><label for='data-159841ef-80f1-49e8-80a4-972e99c965f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.44127792,  0.44127792,  0.44127792, ..., -0.28262954,\n",
       "        -0.28262954, -0.28262954],\n",
       "       [-0.6736082 , -0.45173637, -0.45173637, ..., -0.70635198,\n",
       "        -1.26786956, -0.87499378],\n",
       "       [ 0.55427798,  0.55427798,  0.49077358, ..., -0.87502477,\n",
       "        -0.87502477, -0.87502477],\n",
       "       ...,\n",
       "       [ 0.8965947 ,  1.06981953,  1.18074916, ...,  0.51383304,\n",
       "         0.51383304,  0.51383304],\n",
       "       [ 0.9702864 ,  0.9702864 ,  0.9702864 , ..., -0.8976637 ,\n",
       "        -0.91648287, -0.91648287],\n",
       "       [-0.08460294, -0.08460294, -0.0978703 , ...,  0.15942518,\n",
       "         0.15942518, -0.03056562]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-257b52b0-a4a3-4cc4-bc48-8b7ae4754347' class='xr-section-summary-in' type='checkbox'  checked><label for='section-257b52b0-a4a3-4cc4-bc48-8b7ae4754347' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:53.861857</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats47e3b9d3-93bc-4030-93e8-a0ae53001094\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats47e3b9d3-93bc-4030-93e8-a0ae53001094\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 40, draw: 1400)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
       "  * draw     (draw) int64 100 101 102 103 104 105 ... 1495 1496 1497 1498 1499\n",
       "Data variables:\n",
       "    lp       (chain, draw) float64 -14.38 -14.38 -14.38 ... -20.11 -20.11 -16.68\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:53.851570\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4d601abd-bec6-4456-a390-72b2cd660b9b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d601abd-bec6-4456-a390-72b2cd660b9b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-801f0def-769b-4215-8e46-608ace91d688' class='xr-section-summary-in' type='checkbox'  checked><label for='section-801f0def-769b-4215-8e46-608ace91d688' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-1a884fe7-65d4-4488-9286-2307c0b020f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a884fe7-65d4-4488-9286-2307c0b020f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8614974c-129e-4b2b-a840-b09c15a996fa' class='xr-var-data-in' type='checkbox'><label for='data-8614974c-129e-4b2b-a840-b09c15a996fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>100 101 102 103 ... 1497 1498 1499</div><input id='attrs-5350a663-8d50-4fc2-af5f-df6e4c4f5192' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5350a663-8d50-4fc2-af5f-df6e4c4f5192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29e88c2e-d4fb-4247-ae8d-e31ec3ba5765' class='xr-var-data-in' type='checkbox'><label for='data-29e88c2e-d4fb-4247-ae8d-e31ec3ba5765' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 100,  101,  102, ..., 1497, 1498, 1499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-952eaeb7-9ef7-4d67-8d1e-4a3043496a63' class='xr-section-summary-in' type='checkbox'  checked><label for='section-952eaeb7-9ef7-4d67-8d1e-4a3043496a63' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-14.38 -14.38 ... -20.11 -16.68</div><input id='attrs-f39db3b4-f5f5-4e15-9def-cfd92f3fff8d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f39db3b4-f5f5-4e15-9def-cfd92f3fff8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2e32172-c7a6-4df8-abb4-8d2339a99d11' class='xr-var-data-in' type='checkbox'><label for='data-f2e32172-c7a6-4df8-abb4-8d2339a99d11' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-14.37752718, -14.37752718, -14.37752718, ..., -13.44930902,\n",
       "        -13.44930902, -13.44930902],\n",
       "       [-14.27358555, -13.3607929 , -13.3607929 , ..., -13.70035183,\n",
       "        -17.53674728, -14.41060779],\n",
       "       [-15.5233782 , -15.5233782 , -19.77298595, ..., -13.55239988,\n",
       "        -13.55239988, -13.55239988],\n",
       "       ...,\n",
       "       [-15.63000723, -16.74445363, -18.12375562, ..., -14.60346133,\n",
       "        -14.60346133, -14.60346133],\n",
       "       [-17.75897755, -17.75897755, -17.75897755, ..., -17.52355592,\n",
       "        -19.35557577, -19.35557577],\n",
       "       [-16.21284898, -16.21284898, -16.37273547, ..., -20.1093519 ,\n",
       "        -20.1093519 , -16.6765458 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-24bcc845-94aa-4fc0-a631-ecd22a36767a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-24bcc845-94aa-4fc0-a631-ecd22a36767a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:53.851570</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_datae0296fa9-a663-4fde-bceb-82f9402f3a57\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_datae0296fa9-a663-4fde-bceb-82f9402f3a57\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (arg_0_dim_0: 8, arg_1_dim_0: 8)\n",
       "Coordinates:\n",
       "  * arg_0_dim_0  (arg_0_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "  * arg_1_dim_0  (arg_1_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    arg_0        (arg_0_dim_0) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
       "    arg_1        (arg_1_dim_0) float64 15.0 10.0 16.0 11.0 9.0 11.0 10.0 18.0\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:53.853598\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-03d6996a-24e7-4751-97db-928f4a7fd0ec' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-03d6996a-24e7-4751-97db-928f4a7fd0ec' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>arg_0_dim_0</span>: 8</li><li><span class='xr-has-index'>arg_1_dim_0</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-31df9a2a-04e5-47a4-828f-61a3c4d9a212' class='xr-section-summary-in' type='checkbox'  checked><label for='section-31df9a2a-04e5-47a4-828f-61a3c4d9a212' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>arg_0_dim_0</span></div><div class='xr-var-dims'>(arg_0_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-3b750497-dab9-4b6f-b2a7-2d9ccd8a3bbc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b750497-dab9-4b6f-b2a7-2d9ccd8a3bbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5834bbc0-8d36-4a33-934e-8190c64103b3' class='xr-var-data-in' type='checkbox'><label for='data-5834bbc0-8d36-4a33-934e-8190c64103b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>arg_1_dim_0</span></div><div class='xr-var-dims'>(arg_1_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-c731f27c-c4c2-4ec4-bc81-c72a83cd2a73' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c731f27c-c4c2-4ec4-bc81-c72a83cd2a73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da5af05f-24fb-44bd-9123-09bd24af6994' class='xr-var-data-in' type='checkbox'><label for='data-da5af05f-24fb-44bd-9123-09bd24af6994' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55a1eab3-fe72-4ddb-9838-aebd3ef63851' class='xr-section-summary-in' type='checkbox'  checked><label for='section-55a1eab3-fe72-4ddb-9838-aebd3ef63851' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>arg_0</span></div><div class='xr-var-dims'>(arg_0_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0</div><input id='attrs-e0f78b1c-54b6-4be5-864f-bde897615ea2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0f78b1c-54b6-4be5-864f-bde897615ea2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8131889-635b-46ec-9c5c-711fd8d2b03f' class='xr-var-data-in' type='checkbox'><label for='data-b8131889-635b-46ec-9c5c-711fd8d2b03f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([28.,  8., -3.,  7., -1.,  1., 18., 12.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>arg_1</span></div><div class='xr-var-dims'>(arg_1_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 10.0 16.0 ... 11.0 10.0 18.0</div><input id='attrs-b83c2065-136b-4db4-8f12-9a33ee1cf695' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b83c2065-136b-4db4-8f12-9a33ee1cf695' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b441176-f004-4eab-83cd-3527d86b5b97' class='xr-var-data-in' type='checkbox'><label for='data-1b441176-f004-4eab-83cd-3527d86b5b97' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15., 10., 16., 11.,  9., 11., 10., 18.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-11f4ee59-1c17-4451-b2a3-4c99918bad25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-11f4ee59-1c17-4451-b2a3-4c99918bad25' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:53.853598</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-sections.group-sections {\n",
       "  grid-template-columns: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata1.sel(draw=slice(100, None))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62846712",
   "metadata": {},
   "source": [
    "From an InferenceData object, ArviZ's native data structure, the {func}`posterior plot <arviz.plot_posterior>` of a few variables can be done in one line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "86aa367a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'mu'}>,\n",
       "       <AxesSubplot:title={'center':'tau'}>,\n",
       "       <AxesSubplot:title={'center':'eta4'}>], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2484x552 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_posterior(idata1, var_names=[\"mu\", \"tau\", \"eta4\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a338d42a",
   "metadata": {},
   "source": [
    "## Structuring the posterior as multidimensional variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0969993",
   "metadata": {},
   "source": [
    "This way of calling ``from_emcee`` stores each `eta` as a different variable, called `eta#`, \n",
    "however, they are in fact different dimensions of the same variable. \n",
    "This can be seen in the code of the likelihood and prior functions, where `theta` is unpacked as:\n",
    "\n",
    "    mu, tau, eta = theta[0], theta[1], theta[2:]\n",
    "    \n",
    "ArviZ has support for multidimensional variables, and there is a way to tell it how to split the variables like it was done in the likelihood and prior functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bdb9ed86",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       ".xr-var-data,\n",
       ".xr-attrs {\n",
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       ".xr-attrs,\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (chain: 40, draw: 1500, var_2_dim_0: 8)\n",
       "Coordinates:\n",
       "  * chain        (chain) int64 0 1 2 3 4 5 6 7 8 ... 31 32 33 34 35 36 37 38 39\n",
       "  * draw         (draw) int64 0 1 2 3 4 5 6 ... 1494 1495 1496 1497 1498 1499\n",
       "  * var_2_dim_0  (var_2_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    var_0        (chain, draw) float64 0.6982 0.7962 0.8433 ... 5.763 5.029\n",
       "    var_1        (chain, draw) float64 0.6679 0.7259 0.8075 ... 2.051 3.239\n",
       "    var_2        (chain, draw, var_2_dim_0) float64 0.08153 -0.5837 ... -0.03057\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:54.508656\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-05d508a1-6d17-4732-887f-b82c27e79da3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-05d508a1-6d17-4732-887f-b82c27e79da3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li><li><span class='xr-has-index'>var_2_dim_0</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d7d12aa2-5d10-42c5-b4f4-2f07064131a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7d12aa2-5d10-42c5-b4f4-2f07064131a6' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-1e26a068-e446-4ed2-8dbf-c8e0b6761ff0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e26a068-e446-4ed2-8dbf-c8e0b6761ff0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f44b106-5a3f-42c4-8431-2fec2b4cd77b' class='xr-var-data-in' type='checkbox'><label for='data-8f44b106-5a3f-42c4-8431-2fec2b4cd77b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-ba59a40d-fc95-4667-8ba1-183c4c6a7fa4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba59a40d-fc95-4667-8ba1-183c4c6a7fa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14fc6257-bc6a-4635-afd7-9a4e76b2aebd' class='xr-var-data-in' type='checkbox'><label for='data-14fc6257-bc6a-4635-afd7-9a4e76b2aebd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>var_2_dim_0</span></div><div class='xr-var-dims'>(var_2_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-81508331-63b2-4e4d-84a5-3910de8f596a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81508331-63b2-4e4d-84a5-3910de8f596a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6ed18e4-0ee0-4fab-b6ca-6f4d16c9caae' class='xr-var-data-in' type='checkbox'><label for='data-a6ed18e4-0ee0-4fab-b6ca-6f4d16c9caae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8443cc17-db81-4794-acdc-93f6d8154425' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8443cc17-db81-4794-acdc-93f6d8154425' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>var_0</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6982 0.7962 ... 5.763 5.029</div><input id='attrs-6a755700-6218-4deb-94de-bcf10145a25d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6a755700-6218-4deb-94de-bcf10145a25d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74d8fde3-d55b-4d6e-8976-0dc59ecfe122' class='xr-var-data-in' type='checkbox'><label for='data-74d8fde3-d55b-4d6e-8976-0dc59ecfe122' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.69824844,  0.79617544,  0.84330726, ...,  5.36725902,\n",
       "         5.36725902,  5.36725902],\n",
       "       [ 0.11248859,  0.11248859,  0.06015108, ...,  6.26414247,\n",
       "         7.48213444,  7.62666694],\n",
       "       [-0.93632231, -0.93632231, -0.88874485, ...,  2.07279112,\n",
       "         2.07279112,  2.07279112],\n",
       "       ...,\n",
       "       [-1.39513971, -0.6841303 , -0.22676674, ...,  8.03948904,\n",
       "         8.03948904,  8.03948904],\n",
       "       [-0.64258473, -0.63895969, -0.27810694, ...,  1.28046414,\n",
       "        -0.03145993, -0.03145993],\n",
       "       [-1.51237728, -1.51237728, -1.51237728, ...,  5.76290275,\n",
       "         5.76290275,  5.02928256]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>var_1</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6679 0.7259 ... 2.051 3.239</div><input id='attrs-3f73a46b-4dab-483b-b4fb-92f7977842ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f73a46b-4dab-483b-b4fb-92f7977842ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2004cc1-ece6-469d-b522-cf56d6e6be9b' class='xr-var-data-in' type='checkbox'><label for='data-b2004cc1-ece6-469d-b522-cf56d6e6be9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.66788403,  0.7259102 ,  0.80752206, ...,  0.03694788,\n",
       "         0.03694788,  0.03694788],\n",
       "       [ 0.3207948 ,  0.3207948 ,  0.41749554, ...,  9.79903119,\n",
       "         8.14156878,  7.60103129],\n",
       "       [ 1.04038851,  1.04038851,  1.06008197, ..., 17.27429606,\n",
       "        17.27429606, 17.27429606],\n",
       "       ...,\n",
       "       [ 1.33245704,  1.06653867,  1.02533509, ...,  0.22325512,\n",
       "         0.22325512,  0.22325512],\n",
       "       [ 0.53704382,  0.49084296,  0.82428818, ...,  0.97799322,\n",
       "         0.71121769,  0.71121769],\n",
       "       [ 1.03024648,  1.03024648,  1.03024648, ...,  2.05085976,\n",
       "         2.05085976,  3.23924793]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>var_2</span></div><div class='xr-var-dims'>(chain, draw, var_2_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08153 -0.5837 ... 0.0377 -0.03057</div><input id='attrs-d9a41c57-1d63-4c6c-bb7b-86d2fc639646' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d9a41c57-1d63-4c6c-bb7b-86d2fc639646' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aaf90544-f335-462e-94b3-7aa28d78a70a' class='xr-var-data-in' type='checkbox'><label for='data-aaf90544-f335-462e-94b3-7aa28d78a70a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.08152575, -0.58367651,  0.10398099, ...,  0.04490954,\n",
       "          0.29826293, -0.58949077],\n",
       "        [ 0.00851919, -0.63579839, -0.00342714, ...,  0.2302036 ,\n",
       "          0.13570644, -0.51653613],\n",
       "        [ 0.00771088, -0.82795929,  0.08644913, ...,  0.17352078,\n",
       "          0.1384578 , -0.60913273],\n",
       "        ...,\n",
       "        [ 0.01908609, -0.33392221,  0.3755985 , ...,  0.30174842,\n",
       "          0.76155516, -0.28262954],\n",
       "        [ 0.01908609, -0.33392221,  0.3755985 , ...,  0.30174842,\n",
       "          0.76155516, -0.28262954],\n",
       "        [ 0.01908609, -0.33392221,  0.3755985 , ...,  0.30174842,\n",
       "          0.76155516, -0.28262954]],\n",
       "\n",
       "       [[ 0.51822161, -0.27190443,  0.7464532 , ..., -1.06344488,\n",
       "          1.27061036, -1.02587602],\n",
       "        [ 0.51822161, -0.27190443,  0.7464532 , ..., -1.06344488,\n",
       "          1.27061036, -1.02587602],\n",
       "        [ 0.52702754, -0.82228464,  0.37312608, ..., -1.06039012,\n",
       "          0.71765789, -1.18220311],\n",
       "...\n",
       "        [-0.63261196, -0.32320034,  1.33819298, ..., -0.22083327,\n",
       "         -0.15337504, -0.8976637 ],\n",
       "        [-0.78396615, -0.38804561,  1.50462748, ..., -0.23227815,\n",
       "         -0.04459114, -0.91648287],\n",
       "        [-0.78396615, -0.38804561,  1.50462748, ..., -0.23227815,\n",
       "         -0.04459114, -0.91648287]],\n",
       "\n",
       "       [[-0.40230539, -0.34432128, -0.9050526 , ...,  0.00442761,\n",
       "         -0.1805794 ,  1.80343844],\n",
       "        [-0.40230539, -0.34432128, -0.9050526 , ...,  0.00442761,\n",
       "         -0.1805794 ,  1.80343844],\n",
       "        [-0.40230539, -0.34432128, -0.9050526 , ...,  0.00442761,\n",
       "         -0.1805794 ,  1.80343844],\n",
       "        ...,\n",
       "        [ 0.46842652,  1.43086564, -1.05638496, ..., -0.81374118,\n",
       "         -0.20851802,  0.15942518],\n",
       "        [ 0.46842652,  1.43086564, -1.05638496, ..., -0.81374118,\n",
       "         -0.20851802,  0.15942518],\n",
       "        [ 0.60565958,  1.60761732, -0.83442841, ..., -0.53590256,\n",
       "          0.0377028 , -0.03056562]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16f4b786-f2c0-4ad8-ab1d-1dfe6786f625' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16f4b786-f2c0-4ad8-ab1d-1dfe6786f625' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:54.508656</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_statsd443e041-ab3f-4a0a-abcf-a7aa00e650d5\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_statsd443e041-ab3f-4a0a-abcf-a7aa00e650d5\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
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       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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       "\n",
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       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-header > div,\n",
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       "  display: inline;\n",
       "  margin-top: 0;\n",
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       "\n",
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       "\n",
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       "\n",
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       "  font-size: 11px;\n",
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       "\n",
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       "\n",
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       "  grid-column: 1;\n",
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       "\n",
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       "\n",
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       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
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       "\n",
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       "\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 40, draw: 1500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 ... 1493 1494 1495 1496 1497 1498 1499\n",
       "Data variables:\n",
       "    lp       (chain, draw) float64 -16.3 -17.83 -18.92 ... -20.11 -20.11 -16.68\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:54.505081\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d170a4c8-afa1-4d8f-98ea-22daf11e3ab2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d170a4c8-afa1-4d8f-98ea-22daf11e3ab2' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-96fc7a65-b4be-40b2-8980-01461bbb125b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-96fc7a65-b4be-40b2-8980-01461bbb125b' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-e684aed4-a409-4132-a939-d480b6740735' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e684aed4-a409-4132-a939-d480b6740735' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0a81e44-ef73-441e-9cac-66d27430de32' class='xr-var-data-in' type='checkbox'><label for='data-c0a81e44-ef73-441e-9cac-66d27430de32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-94e824bc-2de6-4463-a114-142b896bb1b3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-94e824bc-2de6-4463-a114-142b896bb1b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86825025-e643-42bc-84a8-7052ac23cfeb' class='xr-var-data-in' type='checkbox'><label for='data-86825025-e643-42bc-84a8-7052ac23cfeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea34f54b-6355-446f-b178-b8e964b56b48' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ea34f54b-6355-446f-b178-b8e964b56b48' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-16.3 -17.83 ... -20.11 -16.68</div><input id='attrs-fb7437f0-7a13-42f8-a103-252f5a039e1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb7437f0-7a13-42f8-a103-252f5a039e1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ce69ca7-510e-48ff-adb8-9eb495057ad2' class='xr-var-data-in' type='checkbox'><label for='data-5ce69ca7-510e-48ff-adb8-9eb495057ad2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-16.29703737, -17.82792262, -18.92214387, ..., -13.44930902,\n",
       "        -13.44930902, -13.44930902],\n",
       "       [-23.84232789, -23.84232789, -21.27484126, ..., -13.70035183,\n",
       "        -17.53674728, -14.41060779],\n",
       "       [-23.52855761, -23.52855761, -22.5780883 , ..., -13.55239988,\n",
       "        -13.55239988, -13.55239988],\n",
       "       ...,\n",
       "       [-25.50638851, -17.64822888, -15.83134714, ..., -14.60346133,\n",
       "        -14.60346133, -14.60346133],\n",
       "       [-25.11716475, -26.98472303, -21.36045048, ..., -17.52355592,\n",
       "        -19.35557577, -19.35557577],\n",
       "       [-20.86324354, -20.86324354, -20.86324354, ..., -20.1093519 ,\n",
       "        -20.1093519 , -16.6765458 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d82fa93a-8b38-4969-a81b-05996f5c027c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d82fa93a-8b38-4969-a81b-05996f5c027c' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:54.505081</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
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       "                  <input id=\"idata_observed_data066829ab-3f70-4b21-865a-d7f202744c3f\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "\n",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (arg_0_dim_0: 8, arg_1_dim_0: 8)\n",
       "Coordinates:\n",
       "  * arg_0_dim_0  (arg_0_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "  * arg_1_dim_0  (arg_1_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    arg_0        (arg_0_dim_0) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
       "    arg_1        (arg_1_dim_0) float64 15.0 10.0 16.0 11.0 9.0 11.0 10.0 18.0\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:54.506402\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d41a7d0f-1e41-469d-9c13-14f2fbd3f05c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d41a7d0f-1e41-469d-9c13-14f2fbd3f05c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>arg_0_dim_0</span>: 8</li><li><span class='xr-has-index'>arg_1_dim_0</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-005f141d-c1a2-4cbb-8d07-403ccb6fd1ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-005f141d-c1a2-4cbb-8d07-403ccb6fd1ce' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>arg_0_dim_0</span></div><div class='xr-var-dims'>(arg_0_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-fc7957fe-53a1-4fbb-9fad-8938a7a95f75' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fc7957fe-53a1-4fbb-9fad-8938a7a95f75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-922cf898-8764-434e-a83b-a39fd8e64615' class='xr-var-data-in' type='checkbox'><label for='data-922cf898-8764-434e-a83b-a39fd8e64615' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>arg_1_dim_0</span></div><div class='xr-var-dims'>(arg_1_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-423eddcf-ed2c-4ecf-b398-92406710d1ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-423eddcf-ed2c-4ecf-b398-92406710d1ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6ca7c61-e62c-451e-b5f8-393e47c9462b' class='xr-var-data-in' type='checkbox'><label for='data-a6ca7c61-e62c-451e-b5f8-393e47c9462b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a7225ca-e48a-4ee7-923d-45d8faf8ef50' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a7225ca-e48a-4ee7-923d-45d8faf8ef50' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>arg_0</span></div><div class='xr-var-dims'>(arg_0_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0</div><input id='attrs-abe3de23-5c2b-4544-aeba-9840fbe5c4e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abe3de23-5c2b-4544-aeba-9840fbe5c4e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52fa75b0-0404-4674-9be3-3c264b4ab115' class='xr-var-data-in' type='checkbox'><label for='data-52fa75b0-0404-4674-9be3-3c264b4ab115' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([28.,  8., -3.,  7., -1.,  1., 18., 12.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>arg_1</span></div><div class='xr-var-dims'>(arg_1_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 10.0 16.0 ... 11.0 10.0 18.0</div><input id='attrs-0af492dd-75e1-4642-a7fd-c9bea404d8b5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0af492dd-75e1-4642-a7fd-c9bea404d8b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4d371a8-316a-4872-85c4-97e783f80cf1' class='xr-var-data-in' type='checkbox'><label for='data-d4d371a8-316a-4872-85c4-97e783f80cf1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15., 10., 16., 11.,  9., 11., 10., 18.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15c1f9a7-c5bf-4d65-88b1-5ccd5658da93' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15c1f9a7-c5bf-4d65-88b1-5ccd5658da93' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:54.506402</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
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      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> sample_stats\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idata2 = az.from_emcee(sampler, slices=[0, 1, slice(2, None)])\n",
    "idata2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a9f608",
   "metadata": {},
   "source": [
    "After checking the default variable names, the trace of one dimension of eta can be plotted using ArviZ syntax:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "783e8c25",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_trace(idata2, var_names=[\"var_2\"], coords={\"var_2_dim_0\": 4});"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96731bb8",
   "metadata": {},
   "source": [
    "## `blobs`: unlock sample stats, posterior predictive and miscellanea"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c2d203",
   "metadata": {},
   "source": [
    "Emcee does not store per-draw sample stats, however, it has a functionality called\n",
    "blobs that allows to store any variable on a per-draw basis. It can be used\n",
    "to store some sample_stats or even posterior_predictive data. \n",
    "\n",
    "You can modify the probability function to use this ``blobs`` functionality and store the pointwise log likelihood,\n",
    "then rerun the sampler using the new function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c02124d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lnprob_8school_blobs(theta, y, s):\n",
    "    prior = log_prior_8school(theta)\n",
    "    like_vect = log_likelihood_8school(theta, y, s)\n",
    "    like = np.sum(like_vect)\n",
    "    return like + prior, like_vect\n",
    "\n",
    "\n",
    "sampler_blobs = emcee.EnsembleSampler(\n",
    "    nwalkers,\n",
    "    ndim,\n",
    "    lnprob_8school_blobs,\n",
    "    args=(y_obs, sigma),\n",
    ")\n",
    "sampler_blobs.run_mcmc(pos, draws);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928030fc",
   "metadata": {},
   "source": [
    "You can now use the `blob_names` argument to indicate how to store this blob-defined variable. As the group is not specified, it will go to sample_stats.\n",
    "Note that the argument blob_names is added to the arguments covered in the previous examples and we are also introducing `coords` and `dims` arguments to show the power and flexibility of the converter. For more on `coords` and `dims` see `page_in_construction`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0bac089b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "              <div class='xr-header'>\n",
       "                <div class=\"xr-obj-type\">arviz.InferenceData</div>\n",
       "              </div>\n",
       "              <ul class=\"xr-sections group-sections\">\n",
       "              \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_posterior3556e912-e715-4e9d-8bbf-be769a3f151b\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_posterior3556e912-e715-4e9d-8bbf-be769a3f151b\" class = \"xr-section-summary\">posterior</label>\n",
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       "\n",
       ".xr-var-data > table {\n",
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       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
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       "\n",
       ".xr-attrs,\n",
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       "\n",
       "dl.xr-attrs {\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  height: 1.5em !important;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 40, draw: 1500, school: 8)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 ... 1493 1494 1495 1496 1497 1498 1499\n",
       "  * school   (school) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    mu       (chain, draw) float64 0.6982 0.7962 0.8433 ... 5.763 5.763 5.029\n",
       "    tau      (chain, draw) float64 0.6679 0.7259 0.8075 ... 2.051 2.051 3.239\n",
       "    eta      (chain, draw, school) float64 0.08153 -0.5837 ... 0.0377 -0.03057\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:57.535401\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bd637e33-f421-4a28-b172-9ce4c4018bec' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd637e33-f421-4a28-b172-9ce4c4018bec' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dd4314f2-9f92-4974-b54c-17bfc329d63a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd4314f2-9f92-4974-b54c-17bfc329d63a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-701278fd-5d40-4132-af31-4e769db92492' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-701278fd-5d40-4132-af31-4e769db92492' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1858e470-b450-4c56-8d0c-90a65e11422c' class='xr-var-data-in' type='checkbox'><label for='data-1858e470-b450-4c56-8d0c-90a65e11422c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-1cee5f87-0cdf-43c7-9817-7a5c261ba4cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1cee5f87-0cdf-43c7-9817-7a5c261ba4cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1001326b-9744-4693-87a5-86ff768aafe1' class='xr-var-data-in' type='checkbox'><label for='data-1001326b-9744-4693-87a5-86ff768aafe1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-2db5b67e-7897-4652-aa76-040c99bc05f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2db5b67e-7897-4652-aa76-040c99bc05f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c252e952-3431-4e71-bb6b-07aecc131d08' class='xr-var-data-in' type='checkbox'><label for='data-c252e952-3431-4e71-bb6b-07aecc131d08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b80e329-83b0-4b7d-9529-dfb396bd28d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5b80e329-83b0-4b7d-9529-dfb396bd28d4' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6982 0.7962 ... 5.763 5.029</div><input id='attrs-68dfcff8-6316-4728-9df3-ceb023ecfad7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68dfcff8-6316-4728-9df3-ceb023ecfad7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ec157df-89c3-4cb4-97a5-3499c23ae63d' class='xr-var-data-in' type='checkbox'><label for='data-3ec157df-89c3-4cb4-97a5-3499c23ae63d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.69824844,  0.79617544,  0.84330726, ...,  5.36725902,\n",
       "         5.36725902,  5.36725902],\n",
       "       [ 0.11248859,  0.11248859,  0.06015108, ...,  6.26414247,\n",
       "         7.48213444,  7.62666694],\n",
       "       [-0.93632231, -0.93632231, -0.88874485, ...,  2.07279112,\n",
       "         2.07279112,  2.07279112],\n",
       "       ...,\n",
       "       [-1.39513971, -0.6841303 , -0.22676674, ...,  8.03948904,\n",
       "         8.03948904,  8.03948904],\n",
       "       [-0.64258473, -0.63895969, -0.27810694, ...,  1.28046414,\n",
       "        -0.03145993, -0.03145993],\n",
       "       [-1.51237728, -1.51237728, -1.51237728, ...,  5.76290275,\n",
       "         5.76290275,  5.02928256]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6679 0.7259 ... 2.051 3.239</div><input id='attrs-84d793f7-905f-4754-a7d6-5d9293ed4c18' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84d793f7-905f-4754-a7d6-5d9293ed4c18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-022fc25b-b5ee-48c8-8e98-de9a7684f579' class='xr-var-data-in' type='checkbox'><label for='data-022fc25b-b5ee-48c8-8e98-de9a7684f579' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.66788403,  0.7259102 ,  0.80752206, ...,  0.03694788,\n",
       "         0.03694788,  0.03694788],\n",
       "       [ 0.3207948 ,  0.3207948 ,  0.41749554, ...,  9.79903119,\n",
       "         8.14156878,  7.60103129],\n",
       "       [ 1.04038851,  1.04038851,  1.06008197, ..., 17.27429606,\n",
       "        17.27429606, 17.27429606],\n",
       "       ...,\n",
       "       [ 1.33245704,  1.06653867,  1.02533509, ...,  0.22325512,\n",
       "         0.22325512,  0.22325512],\n",
       "       [ 0.53704382,  0.49084296,  0.82428818, ...,  0.97799322,\n",
       "         0.71121769,  0.71121769],\n",
       "       [ 1.03024648,  1.03024648,  1.03024648, ...,  2.05085976,\n",
       "         2.05085976,  3.23924793]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta</span></div><div class='xr-var-dims'>(chain, draw, school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08153 -0.5837 ... 0.0377 -0.03057</div><input id='attrs-e429fdff-bd4e-465e-9058-ccd46aa0d808' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e429fdff-bd4e-465e-9058-ccd46aa0d808' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-439ab4e2-6820-49b7-8af4-2ce90b0d30c6' class='xr-var-data-in' type='checkbox'><label for='data-439ab4e2-6820-49b7-8af4-2ce90b0d30c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.08152575, -0.58367651,  0.10398099, ...,  0.04490954,\n",
       "          0.29826293, -0.58949077],\n",
       "        [ 0.00851919, -0.63579839, -0.00342714, ...,  0.2302036 ,\n",
       "          0.13570644, -0.51653613],\n",
       "        [ 0.00771088, -0.82795929,  0.08644913, ...,  0.17352078,\n",
       "          0.1384578 , -0.60913273],\n",
       "        ...,\n",
       "        [ 0.01908609, -0.33392221,  0.3755985 , ...,  0.30174842,\n",
       "          0.76155516, -0.28262954],\n",
       "        [ 0.01908609, -0.33392221,  0.3755985 , ...,  0.30174842,\n",
       "          0.76155516, -0.28262954],\n",
       "        [ 0.01908609, -0.33392221,  0.3755985 , ...,  0.30174842,\n",
       "          0.76155516, -0.28262954]],\n",
       "\n",
       "       [[ 0.51822161, -0.27190443,  0.7464532 , ..., -1.06344488,\n",
       "          1.27061036, -1.02587602],\n",
       "        [ 0.51822161, -0.27190443,  0.7464532 , ..., -1.06344488,\n",
       "          1.27061036, -1.02587602],\n",
       "        [ 0.52702754, -0.82228464,  0.37312608, ..., -1.06039012,\n",
       "          0.71765789, -1.18220311],\n",
       "...\n",
       "        [-0.63261196, -0.32320034,  1.33819298, ..., -0.22083327,\n",
       "         -0.15337504, -0.8976637 ],\n",
       "        [-0.78396615, -0.38804561,  1.50462748, ..., -0.23227815,\n",
       "         -0.04459114, -0.91648287],\n",
       "        [-0.78396615, -0.38804561,  1.50462748, ..., -0.23227815,\n",
       "         -0.04459114, -0.91648287]],\n",
       "\n",
       "       [[-0.40230539, -0.34432128, -0.9050526 , ...,  0.00442761,\n",
       "         -0.1805794 ,  1.80343844],\n",
       "        [-0.40230539, -0.34432128, -0.9050526 , ...,  0.00442761,\n",
       "         -0.1805794 ,  1.80343844],\n",
       "        [-0.40230539, -0.34432128, -0.9050526 , ...,  0.00442761,\n",
       "         -0.1805794 ,  1.80343844],\n",
       "        ...,\n",
       "        [ 0.46842652,  1.43086564, -1.05638496, ..., -0.81374118,\n",
       "         -0.20851802,  0.15942518],\n",
       "        [ 0.46842652,  1.43086564, -1.05638496, ..., -0.81374118,\n",
       "         -0.20851802,  0.15942518],\n",
       "        [ 0.60565958,  1.60761732, -0.83442841, ..., -0.53590256,\n",
       "          0.0377028 , -0.03056562]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2a51736e-2c5b-46af-aab8-5b5294ac5e6b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a51736e-2c5b-46af-aab8-5b5294ac5e6b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:57.535401</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_log_likelihoodf83c074c-eb5d-4512-8849-cd59e97a5d85\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
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       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (chain: 40, draw: 1500, school: 8)\n",
       "Coordinates:\n",
       "  * chain           (chain) int64 0 1 2 3 4 5 6 7 8 ... 32 33 34 35 36 37 38 39\n",
       "  * draw            (draw) int64 0 1 2 3 4 5 6 ... 1494 1495 1496 1497 1498 1499\n",
       "  * school          (school) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    log_likelihood  (chain, draw, school) float64 -3.3 -0.5916 ... -0.1543\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:57.531408\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-dc49297f-caf9-4697-9f65-f937197eaaac' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dc49297f-caf9-4697-9f65-f937197eaaac' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dc791ab7-64b3-4cd9-8579-9595909a2c8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc791ab7-64b3-4cd9-8579-9595909a2c8b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-79bfb43a-7da0-44bc-bb14-201ded6b15cb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-79bfb43a-7da0-44bc-bb14-201ded6b15cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8031772d-364d-4fbf-b0b4-beecc567ba46' class='xr-var-data-in' type='checkbox'><label for='data-8031772d-364d-4fbf-b0b4-beecc567ba46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-0976ef1e-5515-4d71-8f86-413f16639304' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0976ef1e-5515-4d71-8f86-413f16639304' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3272dd18-dd43-4f4b-beb9-d4709600c9e4' class='xr-var-data-in' type='checkbox'><label for='data-3272dd18-dd43-4f4b-beb9-d4709600c9e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-a6f05eee-a5bf-453e-9fb1-73cf454db5e0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6f05eee-a5bf-453e-9fb1-73cf454db5e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9606862f-a29b-49e6-b8f3-0d23b2bcebda' class='xr-var-data-in' type='checkbox'><label for='data-9606862f-a29b-49e6-b8f3-0d23b2bcebda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34bf0672-1068-4e0e-ad59-13bfc6bbc3b4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-34bf0672-1068-4e0e-ad59-13bfc6bbc3b4' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_likelihood</span></div><div class='xr-var-dims'>(chain, draw, school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.3 -0.5916 ... -1.651 -0.1543</div><input id='attrs-af4eba68-e48a-4f6e-be17-ae400b482713' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af4eba68-e48a-4f6e-be17-ae400b482713' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19141a59-209c-4209-9f68-af09f71662a5' class='xr-var-data-in' type='checkbox'><label for='data-19141a59-209c-4209-9f68-af09f71662a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-3.29962425e+00, -5.91603994e-01, -5.54512921e-02, ...,\n",
       "         -6.10346874e-04, -2.92497097e+00, -4.22172393e-01],\n",
       "        [-3.28760730e+00, -5.87576994e-01, -5.62190075e-02, ...,\n",
       "         -1.11418891e-05, -2.92591763e+00, -4.13790819e-01],\n",
       "        [-3.27621247e+00, -6.12351344e-01, -5.98143882e-02, ...,\n",
       "         -2.26937336e-06, -2.90528105e+00, -4.18794556e-01],\n",
       "        ...,\n",
       "        [-2.27648464e+00, -6.99644132e-02, -2.74388479e-01, ...,\n",
       "         -1.58433525e-01, -1.58876020e+00, -1.36209530e-01],\n",
       "        [-2.27648464e+00, -6.99644132e-02, -2.74388479e-01, ...,\n",
       "         -1.58433525e-01, -1.58876020e+00, -1.36209530e-01],\n",
       "        [-2.27648464e+00, -6.99644132e-02, -2.74388479e-01, ...,\n",
       "         -1.58433525e-01, -1.58876020e+00, -1.36209530e-01]],\n",
       "\n",
       "       [[-3.41541659e+00, -6.35964292e-01, -4.38888593e-02, ...,\n",
       "         -1.24760573e-02, -3.05547121e+00, -4.60634226e-01],\n",
       "        [-3.41541659e+00, -6.35964292e-01, -4.38888593e-02, ...,\n",
       "         -1.24760573e-02, -3.05547121e+00, -4.60634226e-01],\n",
       "        [-3.41505897e+00, -6.86105588e-01, -4.03992301e-02, ...,\n",
       "         -1.57972233e-02, -3.11177713e+00, -4.77128919e-01],\n",
       "...\n",
       "        [-3.32168269e+00, -4.94999995e-01, -1.22028296e-01, ...,\n",
       "         -3.43723125e-05, -2.84581232e+00, -4.15125703e-01],\n",
       "        [-3.63258963e+00, -6.90136396e-01, -6.37138926e-02, ...,\n",
       "         -1.18346757e-02, -3.26278253e+00, -4.96498642e-01],\n",
       "        [-3.63258963e+00, -6.90136396e-01, -6.37138926e-02, ...,\n",
       "         -1.18346757e-02, -3.26278253e+00, -4.96498642e-01]],\n",
       "\n",
       "       [[-3.98051738e+00, -9.73599203e-01, -1.20407032e-03, ...,\n",
       "         -5.19763621e-02, -3.88027694e+00, -4.19212449e-01],\n",
       "        [-3.98051738e+00, -9.73599203e-01, -1.20407032e-03, ...,\n",
       "         -5.19763621e-02, -3.88027694e+00, -4.19212449e-01],\n",
       "        [-3.98051738e+00, -9.73599203e-01, -1.20407032e-03, ...,\n",
       "         -5.19763621e-02, -3.88027694e+00, -4.19212449e-01],\n",
       "        ...,\n",
       "        [-2.01193802e+00, -4.86377256e-03, -1.69970952e-01, ...,\n",
       "         -7.91160711e-02, -1.60395600e+00, -1.07807833e-01],\n",
       "        [-2.01193802e+00, -4.86377256e-03, -1.69970952e-01, ...,\n",
       "         -7.91160711e-02, -1.60395600e+00, -1.07807833e-01],\n",
       "        [-1.96164971e+00, -5.00306683e-02, -1.10820832e-01, ...,\n",
       "         -4.34669926e-02, -1.65086232e+00, -1.54262471e-01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-64533825-b5ad-4c2e-9b0f-26a62a672da7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-64533825-b5ad-4c2e-9b0f-26a62a672da7' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:57.531408</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
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       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 40, draw: 1500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 ... 1493 1494 1495 1496 1497 1498 1499\n",
       "Data variables:\n",
       "    lp       (chain, draw) float64 -16.3 -17.83 -18.92 ... -20.11 -20.11 -16.68\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:57.532516\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-45fe1448-358b-45b2-8958-44e2f267485f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45fe1448-358b-45b2-8958-44e2f267485f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6e2fcf27-06e9-4891-b35e-2304b996ac9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e2fcf27-06e9-4891-b35e-2304b996ac9d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-a1d6545a-1ddb-4def-8128-837b0cef9f50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1d6545a-1ddb-4def-8128-837b0cef9f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b585a909-a7c5-4d6e-9458-69f7c9d27aa7' class='xr-var-data-in' type='checkbox'><label for='data-b585a909-a7c5-4d6e-9458-69f7c9d27aa7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-125bb1cb-a29d-4fbb-a091-2e48ce3bb978' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-125bb1cb-a29d-4fbb-a091-2e48ce3bb978' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-189660a0-7bb6-4af3-8b28-a4d0c8a4a6e9' class='xr-var-data-in' type='checkbox'><label for='data-189660a0-7bb6-4af3-8b28-a4d0c8a4a6e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c84895fe-99ee-42bc-ba35-cacf71f02c25' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c84895fe-99ee-42bc-ba35-cacf71f02c25' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-16.3 -17.83 ... -20.11 -16.68</div><input id='attrs-c5f78b9c-7dec-4016-8f3a-2bca7df49965' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c5f78b9c-7dec-4016-8f3a-2bca7df49965' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-485f077f-4a34-4536-ac10-193f34c4f3c3' class='xr-var-data-in' type='checkbox'><label for='data-485f077f-4a34-4536-ac10-193f34c4f3c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-16.29703737, -17.82792262, -18.92214387, ..., -13.44930902,\n",
       "        -13.44930902, -13.44930902],\n",
       "       [-23.84232789, -23.84232789, -21.27484126, ..., -13.70035183,\n",
       "        -17.53674728, -14.41060779],\n",
       "       [-23.52855761, -23.52855761, -22.5780883 , ..., -13.55239988,\n",
       "        -13.55239988, -13.55239988],\n",
       "       ...,\n",
       "       [-25.50638851, -17.64822888, -15.83134714, ..., -14.60346133,\n",
       "        -14.60346133, -14.60346133],\n",
       "       [-25.11716475, -26.98472303, -21.36045048, ..., -17.52355592,\n",
       "        -19.35557577, -19.35557577],\n",
       "       [-20.86324354, -20.86324354, -20.86324354, ..., -20.1093519 ,\n",
       "        -20.1093519 , -16.6765458 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a52e858c-2eeb-441a-bca4-72ad881c9aa6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a52e858c-2eeb-441a-bca4-72ad881c9aa6' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:57.532516</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_data277abdc4-18df-41ea-b984-8c50f7566c4e\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data277abdc4-18df-41ea-b984-8c50f7566c4e\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (arg_0_dim_0: 8, arg_1_dim_0: 8)\n",
       "Coordinates:\n",
       "  * arg_0_dim_0  (arg_0_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "  * arg_1_dim_0  (arg_1_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    arg_0        (arg_0_dim_0) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
       "    arg_1        (arg_1_dim_0) float64 15.0 10.0 16.0 11.0 9.0 11.0 10.0 18.0\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:14:57.533391\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4a2aca0b-5266-407e-b1fa-0e54071f79fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4a2aca0b-5266-407e-b1fa-0e54071f79fd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>arg_0_dim_0</span>: 8</li><li><span class='xr-has-index'>arg_1_dim_0</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8e821a57-ff6e-4908-b370-21cfc25a2a5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e821a57-ff6e-4908-b370-21cfc25a2a5b' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>arg_0_dim_0</span></div><div class='xr-var-dims'>(arg_0_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-10e6d046-f029-4c68-a66e-5ae1405da8fc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10e6d046-f029-4c68-a66e-5ae1405da8fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4de17944-f01b-459f-8e5c-002474a8410d' class='xr-var-data-in' type='checkbox'><label for='data-4de17944-f01b-459f-8e5c-002474a8410d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>arg_1_dim_0</span></div><div class='xr-var-dims'>(arg_1_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-3a4c4df9-fb5e-4039-afa1-45ed7e95c96a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a4c4df9-fb5e-4039-afa1-45ed7e95c96a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9db01908-a019-4bd9-9030-f85b10348cee' class='xr-var-data-in' type='checkbox'><label for='data-9db01908-a019-4bd9-9030-f85b10348cee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37f3ea77-f8bf-46a4-b46b-475107277d83' class='xr-section-summary-in' type='checkbox'  checked><label for='section-37f3ea77-f8bf-46a4-b46b-475107277d83' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>arg_0</span></div><div class='xr-var-dims'>(arg_0_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0</div><input id='attrs-50cf98fc-2dce-49ff-a388-6b58410cdb1c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50cf98fc-2dce-49ff-a388-6b58410cdb1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53d97525-9e4e-442f-8c89-61a86d40e79b' class='xr-var-data-in' type='checkbox'><label for='data-53d97525-9e4e-442f-8c89-61a86d40e79b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([28.,  8., -3.,  7., -1.,  1., 18., 12.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>arg_1</span></div><div class='xr-var-dims'>(arg_1_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 10.0 16.0 ... 11.0 10.0 18.0</div><input id='attrs-1a9f374d-6f33-47d1-a20e-76ac86279bb7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a9f374d-6f33-47d1-a20e-76ac86279bb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49fda10c-4ff6-4c4d-aa0d-d5c8c6141ad6' class='xr-var-data-in' type='checkbox'><label for='data-49fda10c-4ff6-4c4d-aa0d-d5c8c6141ad6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15., 10., 16., 11.,  9., 11., 10., 18.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f0e7675-fdd7-4234-990a-94d416eaeae9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f0e7675-fdd7-4234-990a-94d416eaeae9' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:14:57.533391</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-sections.group-sections {\n",
       "  grid-template-columns: auto;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
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      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> log_likelihood\n",
       "\t> sample_stats\n",
       "\t> observed_data"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dims = {\"eta\": [\"school\"], \"log_likelihood\": [\"school\"]}\n",
    "idata3 = az.from_emcee(\n",
    "    sampler_blobs,\n",
    "    var_names=[\"mu\", \"tau\", \"eta\"],\n",
    "    slices=[0, 1, slice(2, None)],\n",
    "    blob_names=[\"log_likelihood\"],\n",
    "    dims=dims,\n",
    "    coords={\"school\": range(8)},\n",
    ")\n",
    "idata3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e02766e0",
   "metadata": {},
   "source": [
    "## Multi-group blobs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ead8414d",
   "metadata": {},
   "source": [
    "You might even have more complicated blobs, each corresponding to a different group of the InferenceData object. Moreover, you can store the variables passed to the ``EnsembleSampler`` via the ``args`` argument in observed or constant data groups. This is shown in the example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "876594a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.41541659e+00, -6.35964292e-01, -4.38888593e-02, -4.32716958e-01,\n",
       "       -3.27623973e-03, -1.24760573e-02, -3.05547121e+00, -4.60634226e-01])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampler_blobs.blobs[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6d2fec57",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 40, draw: 1500, school: 8)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 ... 1493 1494 1495 1496 1497 1498 1499\n",
       "  * school   (school) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    mu       (chain, draw) float64 0.6982 0.7962 0.8433 ... 5.763 5.763 5.029\n",
       "    tau      (chain, draw) float64 0.6679 0.7259 0.8075 ... 2.051 2.051 3.239\n",
       "    eta      (chain, draw, school) float64 0.08153 -0.5837 ... 0.0377 -0.03057\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:15:01.274909\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4102e938-88ff-42ea-a03b-d1e10cfc08f1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4102e938-88ff-42ea-a03b-d1e10cfc08f1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-01a4d825-ed43-48bf-a554-403f4ae354be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-01a4d825-ed43-48bf-a554-403f4ae354be' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-6b3688cb-2a38-4bec-8f0b-bb6ec319454f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b3688cb-2a38-4bec-8f0b-bb6ec319454f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd32246b-2c87-49b9-a281-b5f4c567dfd9' class='xr-var-data-in' type='checkbox'><label for='data-cd32246b-2c87-49b9-a281-b5f4c567dfd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-9e9aa719-b4f3-4a17-9bf1-df7a26bd2441' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e9aa719-b4f3-4a17-9bf1-df7a26bd2441' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6930d940-d317-425e-910f-70d397d826a4' class='xr-var-data-in' type='checkbox'><label for='data-6930d940-d317-425e-910f-70d397d826a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-eb9eaae3-1e2b-4ff0-9faf-e554da31f317' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb9eaae3-1e2b-4ff0-9faf-e554da31f317' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f967af31-3aee-4117-9f92-21419bf63578' class='xr-var-data-in' type='checkbox'><label for='data-f967af31-3aee-4117-9f92-21419bf63578' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aca6d361-02d1-4767-a433-4aed8484284f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aca6d361-02d1-4767-a433-4aed8484284f' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>mu</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6982 0.7962 ... 5.763 5.029</div><input id='attrs-75d10aab-7720-4432-bdf7-7a2c79d2a340' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-75d10aab-7720-4432-bdf7-7a2c79d2a340' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24df7543-49cc-473c-aece-dceb91d02243' class='xr-var-data-in' type='checkbox'><label for='data-24df7543-49cc-473c-aece-dceb91d02243' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.69824844,  0.79617544,  0.84330726, ...,  5.36725902,\n",
       "         5.36725902,  5.36725902],\n",
       "       [ 0.11248859,  0.11248859,  0.06015108, ...,  6.26414247,\n",
       "         7.48213444,  7.62666694],\n",
       "       [-0.93632231, -0.93632231, -0.88874485, ...,  2.07279112,\n",
       "         2.07279112,  2.07279112],\n",
       "       ...,\n",
       "       [-1.39513971, -0.6841303 , -0.22676674, ...,  8.03948904,\n",
       "         8.03948904,  8.03948904],\n",
       "       [-0.64258473, -0.63895969, -0.27810694, ...,  1.28046414,\n",
       "        -0.03145993, -0.03145993],\n",
       "       [-1.51237728, -1.51237728, -1.51237728, ...,  5.76290275,\n",
       "         5.76290275,  5.02928256]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tau</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6679 0.7259 ... 2.051 3.239</div><input id='attrs-2eaae25a-dbbc-4cf5-a8c6-c7f0dc20d8ac' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2eaae25a-dbbc-4cf5-a8c6-c7f0dc20d8ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ed354a4-f366-46fd-8284-2c57b0881d43' class='xr-var-data-in' type='checkbox'><label for='data-1ed354a4-f366-46fd-8284-2c57b0881d43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 0.66788403,  0.7259102 ,  0.80752206, ...,  0.03694788,\n",
       "         0.03694788,  0.03694788],\n",
       "       [ 0.3207948 ,  0.3207948 ,  0.41749554, ...,  9.79903119,\n",
       "         8.14156878,  7.60103129],\n",
       "       [ 1.04038851,  1.04038851,  1.06008197, ..., 17.27429606,\n",
       "        17.27429606, 17.27429606],\n",
       "       ...,\n",
       "       [ 1.33245704,  1.06653867,  1.02533509, ...,  0.22325512,\n",
       "         0.22325512,  0.22325512],\n",
       "       [ 0.53704382,  0.49084296,  0.82428818, ...,  0.97799322,\n",
       "         0.71121769,  0.71121769],\n",
       "       [ 1.03024648,  1.03024648,  1.03024648, ...,  2.05085976,\n",
       "         2.05085976,  3.23924793]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eta</span></div><div class='xr-var-dims'>(chain, draw, school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.08153 -0.5837 ... 0.0377 -0.03057</div><input id='attrs-ebcc4d2b-9518-4fe2-b12e-4052892ac7dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ebcc4d2b-9518-4fe2-b12e-4052892ac7dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54949346-591a-426e-ad0a-915aa379d88c' class='xr-var-data-in' type='checkbox'><label for='data-54949346-591a-426e-ad0a-915aa379d88c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[ 0.08152575, -0.58367651,  0.10398099, ...,  0.04490954,\n",
       "          0.29826293, -0.58949077],\n",
       "        [ 0.00851919, -0.63579839, -0.00342714, ...,  0.2302036 ,\n",
       "          0.13570644, -0.51653613],\n",
       "        [ 0.00771088, -0.82795929,  0.08644913, ...,  0.17352078,\n",
       "          0.1384578 , -0.60913273],\n",
       "        ...,\n",
       "        [ 0.01908609, -0.33392221,  0.3755985 , ...,  0.30174842,\n",
       "          0.76155516, -0.28262954],\n",
       "        [ 0.01908609, -0.33392221,  0.3755985 , ...,  0.30174842,\n",
       "          0.76155516, -0.28262954],\n",
       "        [ 0.01908609, -0.33392221,  0.3755985 , ...,  0.30174842,\n",
       "          0.76155516, -0.28262954]],\n",
       "\n",
       "       [[ 0.51822161, -0.27190443,  0.7464532 , ..., -1.06344488,\n",
       "          1.27061036, -1.02587602],\n",
       "        [ 0.51822161, -0.27190443,  0.7464532 , ..., -1.06344488,\n",
       "          1.27061036, -1.02587602],\n",
       "        [ 0.52702754, -0.82228464,  0.37312608, ..., -1.06039012,\n",
       "          0.71765789, -1.18220311],\n",
       "...\n",
       "        [-0.63261196, -0.32320034,  1.33819298, ..., -0.22083327,\n",
       "         -0.15337504, -0.8976637 ],\n",
       "        [-0.78396615, -0.38804561,  1.50462748, ..., -0.23227815,\n",
       "         -0.04459114, -0.91648287],\n",
       "        [-0.78396615, -0.38804561,  1.50462748, ..., -0.23227815,\n",
       "         -0.04459114, -0.91648287]],\n",
       "\n",
       "       [[-0.40230539, -0.34432128, -0.9050526 , ...,  0.00442761,\n",
       "         -0.1805794 ,  1.80343844],\n",
       "        [-0.40230539, -0.34432128, -0.9050526 , ...,  0.00442761,\n",
       "         -0.1805794 ,  1.80343844],\n",
       "        [-0.40230539, -0.34432128, -0.9050526 , ...,  0.00442761,\n",
       "         -0.1805794 ,  1.80343844],\n",
       "        ...,\n",
       "        [ 0.46842652,  1.43086564, -1.05638496, ..., -0.81374118,\n",
       "         -0.20851802,  0.15942518],\n",
       "        [ 0.46842652,  1.43086564, -1.05638496, ..., -0.81374118,\n",
       "         -0.20851802,  0.15942518],\n",
       "        [ 0.60565958,  1.60761732, -0.83442841, ..., -0.53590256,\n",
       "          0.0377028 , -0.03056562]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f79e4399-20dc-4f27-948a-7848ec8b7183' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f79e4399-20dc-4f27-948a-7848ec8b7183' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:15:01.274909</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
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       "            \n",
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       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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       "\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 40, draw: 1500, school: 8)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 ... 1493 1494 1495 1496 1497 1498 1499\n",
       "  * school   (school) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    y        (chain, draw, school) float64 2.55 -6.472 -27.52 ... -0.198 23.99\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:15:01.270461\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a42f7333-e9cb-4d7c-84bd-7a263a3352fc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a42f7333-e9cb-4d7c-84bd-7a263a3352fc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-747e1016-55fb-4fc0-aea4-073b2ffca553' class='xr-section-summary-in' type='checkbox'  checked><label for='section-747e1016-55fb-4fc0-aea4-073b2ffca553' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-20f0e1b6-3c2d-4a11-a501-d28037d599af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-20f0e1b6-3c2d-4a11-a501-d28037d599af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d00a478a-3e80-4be4-9d7e-e6f93e362256' class='xr-var-data-in' type='checkbox'><label for='data-d00a478a-3e80-4be4-9d7e-e6f93e362256' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-2f1514a4-cf2b-4097-9a05-238a09e69c55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f1514a4-cf2b-4097-9a05-238a09e69c55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fb7ddfb-04c8-477a-ac78-a465ef3c2236' class='xr-var-data-in' type='checkbox'><label for='data-0fb7ddfb-04c8-477a-ac78-a465ef3c2236' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-44938a14-01a9-4c47-a346-d54d3c894761' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44938a14-01a9-4c47-a346-d54d3c894761' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e66a5baf-902d-42b6-b753-449158af2e08' class='xr-var-data-in' type='checkbox'><label for='data-e66a5baf-902d-42b6-b753-449158af2e08' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-50b77ea2-20f4-439a-9822-e76113994cf5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50b77ea2-20f4-439a-9822-e76113994cf5' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(chain, draw, school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.55 -6.472 -27.52 ... -0.198 23.99</div><input id='attrs-0ab8fe31-823b-4c71-a0c7-0909c01eed3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ab8fe31-823b-4c71-a0c7-0909c01eed3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfb2dbb1-bcad-4592-8247-ec50ba2c7222' class='xr-var-data-in' type='checkbox'><label for='data-dfb2dbb1-bcad-4592-8247-ec50ba2c7222' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[  2.55033076,  -6.47211535, -27.51745706, ...,   6.91654057,\n",
       "           1.43708434,  34.38440582],\n",
       "        [ 10.56568241, -13.09651652,  -0.97103654, ...,   8.26658892,\n",
       "          -1.98414079,  -1.63886384],\n",
       "        [-10.82120742,  -8.81999555,   4.70804147, ...,  15.28034757,\n",
       "           6.92289175, -15.92737786],\n",
       "        ...,\n",
       "        [ 11.94492479,  10.80561966, -20.08957419, ...,  12.83706645,\n",
       "           7.30085292,  11.12453816],\n",
       "        [ 11.94492479,  10.80561966, -20.08957419, ...,  12.83706645,\n",
       "           7.30085292,  11.12453816],\n",
       "        [ 11.94492479,  10.80561966, -20.08957419, ...,  12.83706645,\n",
       "           7.30085292,  11.12453816]],\n",
       "\n",
       "       [[ 16.21807858, -19.11367164, -25.25396006, ...,  -1.51342814,\n",
       "         -14.31565159, -21.57067662],\n",
       "        [ 16.21807858, -19.11367164, -25.25396006, ...,  -1.51342814,\n",
       "         -14.31565159, -21.57067662],\n",
       "        [  5.99596366,  -3.37233749,  14.76088223, ...,   3.03048959,\n",
       "          -7.99827993, -27.87050399],\n",
       "...\n",
       "        [ 25.0713929 ,   0.77571313, -16.857685  , ...,  -7.6442145 ,\n",
       "          -6.94906266, -11.53403959],\n",
       "        [-11.57514461, -14.76384044,  20.59296446, ...,  -8.05191719,\n",
       "           5.25269844, -21.68638323],\n",
       "        [-11.57514461, -14.76384044,  20.59296446, ...,  -8.05191719,\n",
       "           5.25269844, -21.68638323]],\n",
       "\n",
       "       [[-16.08108296,  -5.25404432,  -8.11886536, ...,  12.99207495,\n",
       "           5.32679948,  17.36514081],\n",
       "        [-16.08108296,  -5.25404432,  -8.11886536, ...,  12.99207495,\n",
       "           5.32679948,  17.36514081],\n",
       "        [-16.08108296,  -5.25404432,  -8.11886536, ...,  12.99207495,\n",
       "           5.32679948,  17.36514081],\n",
       "        ...,\n",
       "        [ 16.5054522 ,  -2.42689047,  15.45936197, ...,  15.14612022,\n",
       "          25.58293034,  -8.32778174],\n",
       "        [ 16.5054522 ,  -2.42689047,  15.45936197, ...,  15.14612022,\n",
       "          25.58293034,  -8.32778174],\n",
       "        [  8.66840951,   1.67157617,  -6.29182259, ...,  -5.42734439,\n",
       "          -0.19801562,  23.99112792]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7dae61f7-0fb2-433b-a18b-8f4798693ea4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7dae61f7-0fb2-433b-a18b-8f4798693ea4' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:15:01.270461</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_log_likelihood3bd59750-f641-4f42-9e6c-a8baa4297836\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_log_likelihood3bd59750-f641-4f42-9e6c-a8baa4297836\" class = \"xr-section-summary\">log_likelihood</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
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       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:         (chain: 40, draw: 1500, school: 8)\n",
       "Coordinates:\n",
       "  * chain           (chain) int64 0 1 2 3 4 5 6 7 8 ... 32 33 34 35 36 37 38 39\n",
       "  * draw            (draw) int64 0 1 2 3 4 5 6 ... 1494 1495 1496 1497 1498 1499\n",
       "  * school          (school) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    log_likelihood  (chain, draw, school) float64 -3.3 -0.5916 ... -0.1543\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:15:01.271500\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a32c82f2-7c04-4280-8818-bd7fea08a16a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a32c82f2-7c04-4280-8818-bd7fea08a16a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-63dd22b2-04f0-461c-9eda-5e9fcb82ef37' class='xr-section-summary-in' type='checkbox'  checked><label for='section-63dd22b2-04f0-461c-9eda-5e9fcb82ef37' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-bc3642bc-5cfd-4014-9ea0-7ed6e131d371' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bc3642bc-5cfd-4014-9ea0-7ed6e131d371' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba688a34-74c0-4c09-99fc-53c4b91e8411' class='xr-var-data-in' type='checkbox'><label for='data-ba688a34-74c0-4c09-99fc-53c4b91e8411' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-5e3a48e1-c87d-4c3e-bdc1-126746785827' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e3a48e1-c87d-4c3e-bdc1-126746785827' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e605d527-41a0-464f-97d0-484b870f9f96' class='xr-var-data-in' type='checkbox'><label for='data-e605d527-41a0-464f-97d0-484b870f9f96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-40725165-de8f-44a4-b318-8082161e766f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-40725165-de8f-44a4-b318-8082161e766f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a343dd7d-9e71-4d27-a5dc-1267674aef69' class='xr-var-data-in' type='checkbox'><label for='data-a343dd7d-9e71-4d27-a5dc-1267674aef69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e3765a35-f2de-49fa-899f-191ce8e5092f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e3765a35-f2de-49fa-899f-191ce8e5092f' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>log_likelihood</span></div><div class='xr-var-dims'>(chain, draw, school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.3 -0.5916 ... -1.651 -0.1543</div><input id='attrs-321fb436-12b0-4137-b77f-0fb6079faca8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-321fb436-12b0-4137-b77f-0fb6079faca8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef7f6690-3c1c-495e-b688-49edc162c8a2' class='xr-var-data-in' type='checkbox'><label for='data-ef7f6690-3c1c-495e-b688-49edc162c8a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[-3.29962425e+00, -5.91603994e-01, -5.54512921e-02, ...,\n",
       "         -6.10346874e-04, -2.92497097e+00, -4.22172393e-01],\n",
       "        [-3.28760730e+00, -5.87576994e-01, -5.62190075e-02, ...,\n",
       "         -1.11418891e-05, -2.92591763e+00, -4.13790819e-01],\n",
       "        [-3.27621247e+00, -6.12351344e-01, -5.98143882e-02, ...,\n",
       "         -2.26937336e-06, -2.90528105e+00, -4.18794556e-01],\n",
       "        ...,\n",
       "        [-2.27648464e+00, -6.99644132e-02, -2.74388479e-01, ...,\n",
       "         -1.58433525e-01, -1.58876020e+00, -1.36209530e-01],\n",
       "        [-2.27648464e+00, -6.99644132e-02, -2.74388479e-01, ...,\n",
       "         -1.58433525e-01, -1.58876020e+00, -1.36209530e-01],\n",
       "        [-2.27648464e+00, -6.99644132e-02, -2.74388479e-01, ...,\n",
       "         -1.58433525e-01, -1.58876020e+00, -1.36209530e-01]],\n",
       "\n",
       "       [[-3.41541659e+00, -6.35964292e-01, -4.38888593e-02, ...,\n",
       "         -1.24760573e-02, -3.05547121e+00, -4.60634226e-01],\n",
       "        [-3.41541659e+00, -6.35964292e-01, -4.38888593e-02, ...,\n",
       "         -1.24760573e-02, -3.05547121e+00, -4.60634226e-01],\n",
       "        [-3.41505897e+00, -6.86105588e-01, -4.03992301e-02, ...,\n",
       "         -1.57972233e-02, -3.11177713e+00, -4.77128919e-01],\n",
       "...\n",
       "        [-3.32168269e+00, -4.94999995e-01, -1.22028296e-01, ...,\n",
       "         -3.43723125e-05, -2.84581232e+00, -4.15125703e-01],\n",
       "        [-3.63258963e+00, -6.90136396e-01, -6.37138926e-02, ...,\n",
       "         -1.18346757e-02, -3.26278253e+00, -4.96498642e-01],\n",
       "        [-3.63258963e+00, -6.90136396e-01, -6.37138926e-02, ...,\n",
       "         -1.18346757e-02, -3.26278253e+00, -4.96498642e-01]],\n",
       "\n",
       "       [[-3.98051738e+00, -9.73599203e-01, -1.20407032e-03, ...,\n",
       "         -5.19763621e-02, -3.88027694e+00, -4.19212449e-01],\n",
       "        [-3.98051738e+00, -9.73599203e-01, -1.20407032e-03, ...,\n",
       "         -5.19763621e-02, -3.88027694e+00, -4.19212449e-01],\n",
       "        [-3.98051738e+00, -9.73599203e-01, -1.20407032e-03, ...,\n",
       "         -5.19763621e-02, -3.88027694e+00, -4.19212449e-01],\n",
       "        ...,\n",
       "        [-2.01193802e+00, -4.86377256e-03, -1.69970952e-01, ...,\n",
       "         -7.91160711e-02, -1.60395600e+00, -1.07807833e-01],\n",
       "        [-2.01193802e+00, -4.86377256e-03, -1.69970952e-01, ...,\n",
       "         -7.91160711e-02, -1.60395600e+00, -1.07807833e-01],\n",
       "        [-1.96164971e+00, -5.00306683e-02, -1.10820832e-01, ...,\n",
       "         -4.34669926e-02, -1.65086232e+00, -1.54262471e-01]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e1e48dc-1848-411f-aee0-5ab07a601e9a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e1e48dc-1848-411f-aee0-5ab07a601e9a' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:15:01.271500</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_sample_stats22eb2549-813c-4f64-b876-6623a165d2f9\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_sample_stats22eb2549-813c-4f64-b876-6623a165d2f9\" class = \"xr-section-summary\">sample_stats</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
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       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (chain: 40, draw: 1500)\n",
       "Coordinates:\n",
       "  * chain    (chain) int64 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 37 38 39\n",
       "  * draw     (draw) int64 0 1 2 3 4 5 6 7 ... 1493 1494 1495 1496 1497 1498 1499\n",
       "Data variables:\n",
       "    lp       (chain, draw) float64 -16.3 -17.83 -18.92 ... -20.11 -20.11 -16.68\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:15:01.272272\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7481caa6-c1e7-4c72-bbc7-57f5e06a3d92' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7481caa6-c1e7-4c72-bbc7-57f5e06a3d92' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>chain</span>: 40</li><li><span class='xr-has-index'>draw</span>: 1500</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-00453e1f-c2e7-4deb-ba41-b15e53ae7afe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-00453e1f-c2e7-4deb-ba41-b15e53ae7afe' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>chain</span></div><div class='xr-var-dims'>(chain)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 34 35 36 37 38 39</div><input id='attrs-197073bd-6218-4262-ab75-c27112350193' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-197073bd-6218-4262-ab75-c27112350193' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21da844f-5d05-49fe-9c98-3724581c5667' class='xr-var-data-in' type='checkbox'><label for='data-21da844f-5d05-49fe-9c98-3724581c5667' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>draw</span></div><div class='xr-var-dims'>(draw)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1496 1497 1498 1499</div><input id='attrs-047efa85-7db5-479b-a305-dddc152f085b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-047efa85-7db5-479b-a305-dddc152f085b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8b163ce-83ec-4df1-b166-4d95eee1de7d' class='xr-var-data-in' type='checkbox'><label for='data-d8b163ce-83ec-4df1-b166-4d95eee1de7d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1497, 1498, 1499])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16744d86-ba96-46f7-878b-0eb29cfd92da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16744d86-ba96-46f7-878b-0eb29cfd92da' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lp</span></div><div class='xr-var-dims'>(chain, draw)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-16.3 -17.83 ... -20.11 -16.68</div><input id='attrs-7e341f77-9237-4384-94dd-0b0784ebc6e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e341f77-9237-4384-94dd-0b0784ebc6e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1dceb5f1-b5d0-4b98-aa54-a04191cbdd84' class='xr-var-data-in' type='checkbox'><label for='data-1dceb5f1-b5d0-4b98-aa54-a04191cbdd84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-16.29703737, -17.82792262, -18.92214387, ..., -13.44930902,\n",
       "        -13.44930902, -13.44930902],\n",
       "       [-23.84232789, -23.84232789, -21.27484126, ..., -13.70035183,\n",
       "        -17.53674728, -14.41060779],\n",
       "       [-23.52855761, -23.52855761, -22.5780883 , ..., -13.55239988,\n",
       "        -13.55239988, -13.55239988],\n",
       "       ...,\n",
       "       [-25.50638851, -17.64822888, -15.83134714, ..., -14.60346133,\n",
       "        -14.60346133, -14.60346133],\n",
       "       [-25.11716475, -26.98472303, -21.36045048, ..., -17.52355592,\n",
       "        -19.35557577, -19.35557577],\n",
       "       [-20.86324354, -20.86324354, -20.86324354, ..., -20.1093519 ,\n",
       "        -20.1093519 , -16.6765458 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-09ba2cf4-7fce-4337-aa09-4f3899c2e001' class='xr-section-summary-in' type='checkbox'  checked><label for='section-09ba2cf4-7fce-4337-aa09-4f3899c2e001' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:15:01.272272</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
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       "            \n",
       "            <li class = \"xr-section-item\">\n",
       "                  <input id=\"idata_observed_data78ed3d6e-3132-4f40-b4c0-144e64fe6608\" class=\"xr-section-summary-in\" type=\"checkbox\">\n",
       "                  <label for=\"idata_observed_data78ed3d6e-3132-4f40-b4c0-144e64fe6608\" class = \"xr-section-summary\">observed_data</label>\n",
       "                  <div class=\"xr-section-inline-details\"></div>\n",
       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
       "                          <div style=\"padding-left:2rem;\"><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
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       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (school: 8)\n",
       "Coordinates:\n",
       "  * school   (school) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    y        (school) float64 28.0 8.0 -3.0 7.0 -1.0 1.0 18.0 12.0\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:15:01.273600\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-438895df-9c30-4728-9a48-174d53cefbf7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-438895df-9c30-4728-9a48-174d53cefbf7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>school</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-abcc9b1b-b5ab-429d-b536-ce4dd2b3fc5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-abcc9b1b-b5ab-429d-b536-ce4dd2b3fc5b' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>school</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-c2b5f4ba-a0de-4ee2-8697-1bca1e769fa8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c2b5f4ba-a0de-4ee2-8697-1bca1e769fa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a017f9c9-0ff4-4c01-9886-ef425b1be7b6' class='xr-var-data-in' type='checkbox'><label for='data-a017f9c9-0ff4-4c01-9886-ef425b1be7b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4c4929a0-5811-4499-bbd1-d23a2e3065b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4c4929a0-5811-4499-bbd1-d23a2e3065b6' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y</span></div><div class='xr-var-dims'>(school)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>28.0 8.0 -3.0 7.0 ... 1.0 18.0 12.0</div><input id='attrs-44a3b792-a7e0-45d8-87ee-32d01866279f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44a3b792-a7e0-45d8-87ee-32d01866279f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79e37c72-1306-41ed-b5b4-8b5dc8778bf6' class='xr-var-data-in' type='checkbox'><label for='data-79e37c72-1306-41ed-b5b4-8b5dc8778bf6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([28.,  8., -3.,  7., -1.,  1., 18., 12.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c22833a6-5cfc-42fd-a945-728ebffb903e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c22833a6-5cfc-42fd-a945-728ebffb903e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:15:01.273600</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
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       "                  <div class=\"xr-section-details\">\n",
       "                      <ul id=\"xr-dataset-coord-list\" class=\"xr-var-list\">\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-summary-in + label:before {\n",
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       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
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       "\n",
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       "\n",
       ".xr-section-summary,\n",
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       "  padding-bottom: 4px;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-array-data {\n",
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       "\n",
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       "  display: none;\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "  display: inline-block;\n",
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       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
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       "\n",
       ".xr-dim-list li {\n",
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       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       ".xr-var-data {\n",
       "  display: none;\n",
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       "\n",
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       ".xr-var-data-in:checked ~ .xr-var-data {\n",
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       "\n",
       ".xr-var-data > table {\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
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       "\n",
       ".xr-attrs,\n",
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       ".xr-var-data {\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (sigma_dim_0: 8)\n",
       "Coordinates:\n",
       "  * sigma_dim_0  (sigma_dim_0) int64 0 1 2 3 4 5 6 7\n",
       "Data variables:\n",
       "    sigma        (sigma_dim_0) float64 15.0 10.0 16.0 11.0 9.0 11.0 10.0 18.0\n",
       "Attributes:\n",
       "    created_at:                 2021-08-30T18:15:01.273208\n",
       "    arviz_version:              0.11.2\n",
       "    inference_library:          emcee\n",
       "    inference_library_version:  3.1.1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-027aca59-84da-4560-bc64-e0b8dc88b3c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-027aca59-84da-4560-bc64-e0b8dc88b3c0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>sigma_dim_0</span>: 8</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-971b1dee-c95c-4dae-b182-d37c94440063' class='xr-section-summary-in' type='checkbox'  checked><label for='section-971b1dee-c95c-4dae-b182-d37c94440063' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sigma_dim_0</span></div><div class='xr-var-dims'>(sigma_dim_0)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7</div><input id='attrs-48d83001-99d6-4f53-8d3f-87643b83ed49' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48d83001-99d6-4f53-8d3f-87643b83ed49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-704b3ed1-1b63-4535-a383-5f9f23fe5089' class='xr-var-data-in' type='checkbox'><label for='data-704b3ed1-1b63-4535-a383-5f9f23fe5089' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ccc5ad1-002f-4963-bec9-e6e9c3dcf82a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ccc5ad1-002f-4963-bec9-e6e9c3dcf82a' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sigma</span></div><div class='xr-var-dims'>(sigma_dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 10.0 16.0 ... 11.0 10.0 18.0</div><input id='attrs-9e8811f2-e2e6-4604-8be5-a331b5e02b92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9e8811f2-e2e6-4604-8be5-a331b5e02b92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc565dcd-9fff-4c14-aab6-5993469578fb' class='xr-var-data-in' type='checkbox'><label for='data-fc565dcd-9fff-4c14-aab6-5993469578fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([15., 10., 16., 11.,  9., 11., 10., 18.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ad6b1a67-2e25-41fb-85a7-29043b4add9c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad6b1a67-2e25-41fb-85a7-29043b4add9c' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>created_at :</span></dt><dd>2021-08-30T18:15:01.273208</dd><dt><span>arviz_version :</span></dt><dd>0.11.2</dd><dt><span>inference_library :</span></dt><dd>emcee</dd><dt><span>inference_library_version :</span></dt><dd>3.1.1</dd></dl></div></li></ul></div></div><br></div>\n",
       "                      </ul>\n",
       "                  </div>\n",
       "            </li>\n",
       "            \n",
       "              </ul>\n",
       "            </div>\n",
       "            <style> /* CSS stylesheet for displaying InferenceData objects in jupyterlab.\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-summary > span {\n",
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       "  padding-left: 0.5em;\n",
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       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-array-wrap > label {\n",
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       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt, dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       ".xr-wrap{width:700px!important;} </style>"
      ],
      "text/plain": [
       "Inference data with groups:\n",
       "\t> posterior\n",
       "\t> posterior_predictive\n",
       "\t> log_likelihood\n",
       "\t> sample_stats\n",
       "\t> observed_data\n",
       "\t> constant_data"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def lnprob_8school_blobs(theta, y, sigma):\n",
    "    mu, tau, eta = theta[0], theta[1], theta[2:]\n",
    "    prior = log_prior_8school(theta)\n",
    "    like_vect = log_likelihood_8school(theta, y, sigma)\n",
    "    like = np.sum(like_vect)\n",
    "    # store pointwise log likelihood, useful for model comparison with az.loo or az.waic\n",
    "    # and posterior predictive samples as blobs\n",
    "    return like + prior, (like_vect, np.random.normal((mu + tau * eta), sigma))\n",
    "\n",
    "\n",
    "sampler_blobs = emcee.EnsembleSampler(\n",
    "    nwalkers,\n",
    "    ndim,\n",
    "    lnprob_8school_blobs,\n",
    "    args=(y_obs, sigma),\n",
    ")\n",
    "sampler_blobs.run_mcmc(pos, draws)\n",
    "\n",
    "dims = {\"eta\": [\"school\"], \"log_likelihood\": [\"school\"], \"y\": [\"school\"]}\n",
    "idata4 = az.from_emcee(\n",
    "    sampler_blobs,\n",
    "    var_names=[\"mu\", \"tau\", \"eta\"],\n",
    "    slices=[0, 1, slice(2, None)],\n",
    "    arg_names=[\"y\", \"sigma\"],\n",
    "    arg_groups=[\"observed_data\", \"constant_data\"],\n",
    "    blob_names=[\"log_likelihood\", \"y\"],\n",
    "    blob_groups=[\"log_likelihood\", \"posterior_predictive\"],\n",
    "    dims=dims,\n",
    "    coords={\"school\": range(8)},\n",
    ")\n",
    "idata4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b59d48",
   "metadata": {},
   "source": [
    "This last version, which contains both observed data and posterior predictive can be used to plot posterior predictive checks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c9f059a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "az.plot_ppc(idata4, var_names=[\"y\"], alpha=0.3, num_pp_samples=200);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ac91b263",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Mon Aug 30 2021\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.8.6\n",
      "IPython version      : 7.27.0\n",
      "\n",
      "emcee: 3.1.1\n",
      "arviz: 0.11.2\n",
      "numpy: None\n",
      "\n",
      "Watermark: 2.1.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd0cb61e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}