arviz.InferenceData.add_groups

InferenceData.add_groups(group_dict=None, coords=None, dims=None, **kwargs)

Add new groups to InferenceData object.

Parameters

group_dictdict of {strdict or xarray.Dataset}, optional

Groups to be added

coordsdict of {strarray_like}, optional

Coordinates for the dataset

dimsdict of {strlist of str}, optional

Dimensions of each variable. The keys are variable names, values are lists of coordinates.

kwargsdict, optional

The keyword arguments form of group_dict. One of group_dict or kwargs must be provided.

See also

extend

Extend InferenceData with groups from another InferenceData.

concat

Concatenate InferenceData objects.

Examples

Add a log_likelihood group to the “rugby” example InferenceData after loading. It originally doesn’t have the log_likelihood group:

import arviz as az
idata = az.load_arviz_data("rugby")
idata2 = idata.copy()
post = idata.posterior
obs = idata.observed_data
idata

arviz.InferenceData

• posterior

  <xarray.Dataset>
  Dimensions:    (chain: 4, draw: 500, team: 6)
  Coordinates:
    * chain      (chain) int64 0 1 2 3
    * draw       (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499
    * team       (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      home       (chain, draw) float64 ...
      intercept  (chain, draw) float64 ...
      atts_star  (chain, draw, team) float64 ...
      defs_star  (chain, draw, team) float64 ...
      sd_att     (chain, draw) float64 ...
      sd_def     (chain, draw) float64 ...
      atts       (chain, draw, team) float64 ...
      defs       (chain, draw, team) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.545143
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • team: 6
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • team
      (team)
      object
      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype=object)

  • Data variables: (8)
    • home
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • intercept
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • atts_star
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • defs_star
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • sd_att
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • sd_def
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • atts
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • defs
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • team
      PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.545143

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• posterior_predictive

  <xarray.Dataset>
  Dimensions:      (chain: 4, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 0 1 2 3
    * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * match        (match) object 'Wales Italy' ... 'Ireland England'
  Data variables:
      home_points  (chain, draw, match) int64 ...
      away_points  (chain, draw, match) int64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.563854
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • match: 60
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

  • Data variables: (2)
    • home_points
      (chain, draw, match)
      int64
      ...

      [120000 values with dtype=int64]

    • away_points
      (chain, draw, match)
      int64
      ...

      [120000 values with dtype=int64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.563854

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• sample_stats

  <xarray.Dataset>
  Dimensions:           (chain: 4, draw: 500)
  Coordinates:
    * chain             (chain) int64 0 1 2 3
    * draw              (draw) int64 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
  Data variables:
      energy_error      (chain, draw) float64 ...
      energy            (chain, draw) float64 ...
      tree_size         (chain, draw) float64 ...
      tune              (chain, draw) bool ...
      mean_tree_accept  (chain, draw) float64 ...
      lp                (chain, draw) float64 ...
      depth             (chain, draw) int64 ...
      max_energy_error  (chain, draw) float64 ...
      step_size         (chain, draw) float64 ...
      step_size_bar     (chain, draw) float64 ...
      diverging         (chain, draw) bool ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.555203
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
  • Coordinates: (2)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

  • Data variables: (11)
    • energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • energy
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • tree_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • tune
      (chain, draw)
      bool
      ...

      [2000 values with dtype=bool]

    • mean_tree_accept
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • lp
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • depth
      (chain, draw)
      int64
      ...

      [2000 values with dtype=int64]

    • max_energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size_bar
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • diverging
      (chain, draw)
      bool
      ...

      [2000 values with dtype=bool]

  • Indexes: (2)
    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.555203

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• prior

  <xarray.Dataset>
  Dimensions:       (chain: 1, draw: 500, team: 6, match: 60)
  Coordinates:
    * chain         (chain) int64 0
    * draw          (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team          (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
    * match         (match) object 'Wales Italy' ... 'Ireland England'
  Data variables:
      sd_att_log__  (chain, draw) float64 ...
      intercept     (chain, draw) float64 ...
      atts_star     (chain, draw, team) float64 ...
      defs_star     (chain, draw, team) float64 ...
      away_points   (chain, draw, match) int64 ...
      sd_att        (chain, draw) float64 ...
      sd_def_log__  (chain, draw) float64 ...
      home          (chain, draw) float64 ...
      atts          (chain, draw, team) float64 ...
      sd_def        (chain, draw) float64 ...
      home_points   (chain, draw, match) int64 ...
      defs          (chain, draw, team) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.573731
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 1
    • draw: 500
    • team: 6
    • match: 60
  • Coordinates: (4)
    • chain
      (chain)
      int64
      0

      array([0])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • team
      (team)
      object
      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype=object)

    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

  • Data variables: (12)
    • sd_att_log__
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • intercept
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • atts_star
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • defs_star
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • away_points
      (chain, draw, match)
      int64
      ...

      [30000 values with dtype=int64]

    • sd_att
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • sd_def_log__
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • home
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • atts
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • sd_def
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • home_points
      (chain, draw, match)
      int64
      ...

      [30000 values with dtype=int64]

    • defs
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

  • Indexes: (4)
    • chain
      PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • team
      PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.573731

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• observed_data

  <xarray.Dataset>
  Dimensions:      (match: 60)
  Coordinates:
    * match        (match) object 'Wales Italy' ... 'Ireland England'
  Data variables:
      home_points  (match) float64 ...
      away_points  (match) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.581293
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • match: 60
  • Coordinates: (1)
    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

  • Data variables: (2)
    • home_points
      (match)
      float64
      ...

      [60 values with dtype=float64]

    • away_points
      (match)
      float64
      ...

      [60 values with dtype=float64]

  • Indexes: (1)
    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.581293

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  

Knowing the model, we can compute it manually. In this case however, we will generate random samples with the right shape.

import numpy as np
rng = np.random.default_rng(73)
ary = rng.normal(size=(post.dims["chain"], post.dims["draw"], obs.dims["match"]))
idata.add_groups(
    log_likelihood={"home_points": ary},
    dims={"home_points": ["match"]},
)
idata

arviz.InferenceData

• posterior

  <xarray.Dataset>
  Dimensions:    (chain: 4, draw: 500, team: 6)
  Coordinates:
    * chain      (chain) int64 0 1 2 3
    * draw       (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499
    * team       (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      home       (chain, draw) float64 ...
      intercept  (chain, draw) float64 ...
      atts_star  (chain, draw, team) float64 ...
      defs_star  (chain, draw, team) float64 ...
      sd_att     (chain, draw) float64 ...
      sd_def     (chain, draw) float64 ...
      atts       (chain, draw, team) float64 ...
      defs       (chain, draw, team) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.545143
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • team: 6
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • team
      (team)
      object
      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype=object)

  • Data variables: (8)
    • home
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • intercept
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • atts_star
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • defs_star
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • sd_att
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • sd_def
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • atts
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • defs
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • team
      PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.545143

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• posterior_predictive

  <xarray.Dataset>
  Dimensions:      (chain: 4, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 0 1 2 3
    * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * match        (match) object 'Wales Italy' ... 'Ireland England'
  Data variables:
      home_points  (chain, draw, match) int64 ...
      away_points  (chain, draw, match) int64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.563854
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • match: 60
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

  • Data variables: (2)
    • home_points
      (chain, draw, match)
      int64
      ...

      [120000 values with dtype=int64]

    • away_points
      (chain, draw, match)
      int64
      ...

      [120000 values with dtype=int64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.563854

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• log_likelihood

  <xarray.Dataset>
  Dimensions:      (chain: 4, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 0 1 2 3
    * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * match        (match) int64 0 1 2 3 4 5 6 7 8 ... 51 52 53 54 55 56 57 58 59
  Data variables:
      home_points  (chain, draw, match) float64 -1.093 0.7781 ... 0.2405 1.643
  Attributes:
      created_at:     2023-07-18T19:52:42.552711
      arviz_version:  0.16.1

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • match: 60
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • match
      (match)
      int64
      0 1 2 3 4 5 6 ... 54 55 56 57 58 59

      array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
             36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
             54, 55, 56, 57, 58, 59])

  • Data variables: (1)
    • home_points
      (chain, draw, match)
      float64
      -1.093 0.7781 ... 0.2405 1.643

      array([[[-1.09330591e+00,  7.78087832e-01, -3.81610306e-01, ...,
                4.84564099e-01, -1.61693937e+00, -4.19842670e-01],
              [-2.00619024e-01, -1.70319742e-01, -2.46113915e-01, ...,
               -1.33266766e+00,  1.80447925e+00, -1.45660509e-01],
              [-2.50712206e+00,  1.79834383e+00,  6.30248678e-01, ...,
               -1.02798516e+00,  1.14356634e+00, -3.50814501e-01],
              ...,
              [ 9.92650794e-01, -1.16693044e+00,  5.99931728e-01, ...,
               -3.48188969e-01,  7.35989921e-01, -8.99216695e-01],
              [-4.51116486e-01,  6.76814555e-01,  5.70786683e-01, ...,
                2.18238000e+00,  1.53865762e+00, -8.74706028e-01],
              [ 1.97950620e-01,  6.17303135e-01,  1.74270395e-03, ...,
               -4.49897157e-01,  6.93927981e-01, -4.03693064e-01]],
      
             [[-5.13438873e-01,  2.06269310e-01, -1.39724013e+00, ...,
               -1.22238899e-01,  5.39511058e-02, -1.41409473e+00],
              [ 2.13292127e-01, -1.25754969e-01, -6.19146328e-01, ...,
               -4.54451615e-01, -9.55102426e-02,  2.46344882e+00],
              [ 1.09944686e+00, -8.36675090e-01, -6.64849313e-01, ...,
                2.06469045e-01, -6.89234364e-01,  1.12970438e+00],
      ...
              [ 1.27649065e+00,  3.26657373e-02, -9.41208951e-02, ...,
               -8.41882764e-01,  4.18612818e-01, -7.45126198e-01],
              [ 1.77302616e+00,  2.40029312e-01, -1.03966264e+00, ...,
                6.70392549e-04,  3.41461316e-01, -3.99132876e-01],
              [-4.90112178e-01,  3.20889351e-02,  5.34696144e-01, ...,
                4.35765135e-01,  7.36805282e-01, -4.66833157e-01]],
      
             [[-7.58144372e-02, -3.20704288e+00,  1.84262524e-01, ...,
                1.37396940e+00, -5.05861694e-01,  1.70371025e+00],
              [-2.22836138e-01, -1.65666088e+00,  8.63975137e-01, ...,
               -4.32667587e-01, -1.62718307e+00, -1.05026418e-01],
              [-6.38274887e-01, -7.06321492e-01, -6.25346451e-01, ...,
               -6.70819376e-02, -3.32003187e-01, -7.02170198e-01],
              ...,
              [-6.50718344e-02,  1.26554918e+00, -6.04798463e-01, ...,
                4.01464480e-01, -8.61955082e-02,  1.23169374e+00],
              [ 9.62742755e-01,  1.69023589e-01, -1.08048616e+00, ...,
                5.23012393e-01, -1.10747981e+00,  8.18173269e-02],
              [-5.08068068e-01,  5.31731357e-01,  9.45057998e-01, ...,
               -1.63968009e-01,  2.40471737e-01,  1.64341233e+00]]])

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • match
      PandasIndex

      PandasIndex(Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
             18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
             36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
             54, 55, 56, 57, 58, 59],
            dtype='int64', name='match'))

  • Attributes: (2)

    created_at :

    2023-07-18T19:52:42.552711

    arviz_version :

    0.16.1

  
  
• sample_stats

  <xarray.Dataset>
  Dimensions:           (chain: 4, draw: 500)
  Coordinates:
    * chain             (chain) int64 0 1 2 3
    * draw              (draw) int64 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
  Data variables:
      energy_error      (chain, draw) float64 ...
      energy            (chain, draw) float64 ...
      tree_size         (chain, draw) float64 ...
      tune              (chain, draw) bool ...
      mean_tree_accept  (chain, draw) float64 ...
      lp                (chain, draw) float64 ...
      depth             (chain, draw) int64 ...
      max_energy_error  (chain, draw) float64 ...
      step_size         (chain, draw) float64 ...
      step_size_bar     (chain, draw) float64 ...
      diverging         (chain, draw) bool ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.555203
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
  • Coordinates: (2)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

  • Data variables: (11)
    • energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • energy
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • tree_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • tune
      (chain, draw)
      bool
      ...

      [2000 values with dtype=bool]

    • mean_tree_accept
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • lp
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • depth
      (chain, draw)
      int64
      ...

      [2000 values with dtype=int64]

    • max_energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size_bar
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • diverging
      (chain, draw)
      bool
      ...

      [2000 values with dtype=bool]

  • Indexes: (2)
    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.555203

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• prior

  <xarray.Dataset>
  Dimensions:       (chain: 1, draw: 500, team: 6, match: 60)
  Coordinates:
    * chain         (chain) int64 0
    * draw          (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team          (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
    * match         (match) object 'Wales Italy' ... 'Ireland England'
  Data variables:
      sd_att_log__  (chain, draw) float64 ...
      intercept     (chain, draw) float64 ...
      atts_star     (chain, draw, team) float64 ...
      defs_star     (chain, draw, team) float64 ...
      away_points   (chain, draw, match) int64 ...
      sd_att        (chain, draw) float64 ...
      sd_def_log__  (chain, draw) float64 ...
      home          (chain, draw) float64 ...
      atts          (chain, draw, team) float64 ...
      sd_def        (chain, draw) float64 ...
      home_points   (chain, draw, match) int64 ...
      defs          (chain, draw, team) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.573731
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 1
    • draw: 500
    • team: 6
    • match: 60
  • Coordinates: (4)
    • chain
      (chain)
      int64
      0

      array([0])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • team
      (team)
      object
      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype=object)

    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

  • Data variables: (12)
    • sd_att_log__
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • intercept
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • atts_star
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • defs_star
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • away_points
      (chain, draw, match)
      int64
      ...

      [30000 values with dtype=int64]

    • sd_att
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • sd_def_log__
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • home
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • atts
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • sd_def
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • home_points
      (chain, draw, match)
      int64
      ...

      [30000 values with dtype=int64]

    • defs
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

  • Indexes: (4)
    • chain
      PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • team
      PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.573731

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• observed_data

  <xarray.Dataset>
  Dimensions:      (match: 60)
  Coordinates:
    * match        (match) object 'Wales Italy' ... 'Ireland England'
  Data variables:
      home_points  (match) float64 ...
      away_points  (match) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.581293
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • match: 60
  • Coordinates: (1)
    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

  • Data variables: (2)
    • home_points
      (match)
      float64
      ...

      [60 values with dtype=float64]

    • away_points
      (match)
      float64
      ...

      [60 values with dtype=float64]

  • Indexes: (1)
    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.581293

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  

This is fine if we have raw data, but a bit inconvenient if we start with labeled data already. Why provide dims and coords manually again? Let’s generate a fake log likelihood (doesn’t match the model but it serves just the same for illustration purposes here) working from the posterior and observed_data groups manually:

import xarray as xr
from xarray_einstats.stats import XrDiscreteRV
from scipy.stats import poisson
dist = XrDiscreteRV(poisson)
log_lik = xr.Dataset()
log_lik["home_points"] = dist.logpmf(obs["home_points"], np.exp(post["atts"]))
idata2.add_groups({"log_likelihood": log_lik})
idata2

arviz.InferenceData

• posterior

  <xarray.Dataset>
  Dimensions:    (chain: 4, draw: 500, team: 6)
  Coordinates:
    * chain      (chain) int64 0 1 2 3
    * draw       (draw) int64 0 1 2 3 4 5 6 7 ... 492 493 494 495 496 497 498 499
    * team       (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      home       (chain, draw) float64 ...
      intercept  (chain, draw) float64 ...
      atts_star  (chain, draw, team) float64 ...
      defs_star  (chain, draw, team) float64 ...
      sd_att     (chain, draw) float64 ...
      sd_def     (chain, draw) float64 ...
      atts       (chain, draw, team) float64 ...
      defs       (chain, draw, team) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.545143
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • team: 6
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • team
      (team)
      object
      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype=object)

  • Data variables: (8)
    • home
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • intercept
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • atts_star
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • defs_star
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • sd_att
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • sd_def
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • atts
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

    • defs
      (chain, draw, team)
      float64
      ...

      [12000 values with dtype=float64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • team
      PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.545143

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• posterior_predictive

  <xarray.Dataset>
  Dimensions:      (chain: 4, draw: 500, match: 60)
  Coordinates:
    * chain        (chain) int64 0 1 2 3
    * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * match        (match) object 'Wales Italy' ... 'Ireland England'
  Data variables:
      home_points  (chain, draw, match) int64 ...
      away_points  (chain, draw, match) int64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.563854
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
    • match: 60
  • Coordinates: (3)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

  • Data variables: (2)
    • home_points
      (chain, draw, match)
      int64
      ...

      [120000 values with dtype=int64]

    • away_points
      (chain, draw, match)
      int64
      ...

      [120000 values with dtype=int64]

  • Indexes: (3)
    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.563854

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• log_likelihood

  <xarray.Dataset>
  Dimensions:      (match: 60, chain: 4, draw: 500, team: 6)
  Coordinates:
    * match        (match) object 'Wales Italy' ... 'Ireland England'
    * chain        (chain) int64 0 1 2 3
    * draw         (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team         (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
  Data variables:
      home_points  (match, chain, draw, team) float64 -50.27 -53.03 ... -21.14

  xarray.Dataset

  • Dimensions:
    • match: 60
    • chain: 4
    • draw: 500
    • team: 6
  • Coordinates: (4)
    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • team
      (team)
      object
      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype=object)

  • Data variables: (1)
    • home_points
      (match, chain, draw, team)
      float64
      -50.27 -53.03 ... -27.08 -21.14

      array([[[[-50.27340679, -53.02763088, -49.60880645, -54.0949587 ,
                -62.83670609, -45.95963245],
               [-49.23489128, -54.43022627, -49.54759518, -54.04058511,
                -60.85259984, -47.65128777],
               [-48.70614082, -53.56169046, -51.3296424 , -55.90686766,
                -59.21475528, -47.02705307],
               ...,
               [-48.38292302, -55.22150328, -51.04573136, -55.98020613,
                -59.30672607, -45.83824038],
               [-50.16387191, -55.24153061, -50.41938842, -53.98620983,
                -58.14264601, -47.76205157],
               [-49.30549847, -54.33225327, -49.49141469, -53.81059892,
                -59.62302777, -49.16254883]],
      
              [[-47.20574061, -53.16969297, -49.904949  , -56.45851665,
                -62.78384919, -46.30966544],
               [-49.72097095, -53.0125925 , -49.97979965, -55.48001996,
                -59.80024005, -47.74533106],
               [-48.65861079, -52.63480405, -51.27588453, -55.48421341,
                -60.87333574, -46.84022256],
      ...
               [-21.07420765, -24.85696218, -22.43288564, -25.56391285,
                -27.16305238, -20.34882743],
               [-21.94692547, -24.43794791, -22.33531041, -24.04555291,
                -27.90171192, -20.75099542],
               [-21.30265712, -24.31455013, -22.22832286, -23.8844833 ,
                -28.41733435, -21.28299039]],
      
              [[-22.0916242 , -23.10668814, -22.21122819, -24.72579545,
                -27.2498818 , -21.99829746],
               [-20.92629642, -24.55922194, -22.07414212, -25.39004721,
                -28.47517395, -20.05681077],
               [-21.77733448, -25.04409811, -22.62294366, -24.90508263,
                -27.31972085, -19.77252911],
               ...,
               [-21.55768786, -24.41427901, -22.0736515 , -24.56115236,
                -28.66848219, -20.18796241],
               [-21.77257294, -24.299161  , -22.29283009, -24.83788118,
                -28.15446192, -20.09145606],
               [-20.96133141, -24.92572979, -22.42301855, -24.88194918,
                -27.08405599, -21.13980755]]]])

  • Indexes: (4)
    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • team
      PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

  • Attributes: (0)

  
  
• sample_stats

  <xarray.Dataset>
  Dimensions:           (chain: 4, draw: 500)
  Coordinates:
    * chain             (chain) int64 0 1 2 3
    * draw              (draw) int64 0 1 2 3 4 5 6 ... 493 494 495 496 497 498 499
  Data variables:
      energy_error      (chain, draw) float64 ...
      energy            (chain, draw) float64 ...
      tree_size         (chain, draw) float64 ...
      tune              (chain, draw) bool ...
      mean_tree_accept  (chain, draw) float64 ...
      lp                (chain, draw) float64 ...
      depth             (chain, draw) int64 ...
      max_energy_error  (chain, draw) float64 ...
      step_size         (chain, draw) float64 ...
      step_size_bar     (chain, draw) float64 ...
      diverging         (chain, draw) bool ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.555203
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 4
    • draw: 500
  • Coordinates: (2)
    • chain
      (chain)
      int64
      0 1 2 3

      array([0, 1, 2, 3])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

  • Data variables: (11)
    • energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • energy
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • tree_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • tune
      (chain, draw)
      bool
      ...

      [2000 values with dtype=bool]

    • mean_tree_accept
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • lp
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • depth
      (chain, draw)
      int64
      ...

      [2000 values with dtype=int64]

    • max_energy_error
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • step_size_bar
      (chain, draw)
      float64
      ...

      [2000 values with dtype=float64]

    • diverging
      (chain, draw)
      bool
      ...

      [2000 values with dtype=bool]

  • Indexes: (2)
    • chain
      PandasIndex

      PandasIndex(Index([0, 1, 2, 3], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.555203

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• prior

  <xarray.Dataset>
  Dimensions:       (chain: 1, draw: 500, team: 6, match: 60)
  Coordinates:
    * chain         (chain) int64 0
    * draw          (draw) int64 0 1 2 3 4 5 6 7 ... 493 494 495 496 497 498 499
    * team          (team) object 'Wales' 'France' 'Ireland' ... 'Italy' 'England'
    * match         (match) object 'Wales Italy' ... 'Ireland England'
  Data variables:
      sd_att_log__  (chain, draw) float64 ...
      intercept     (chain, draw) float64 ...
      atts_star     (chain, draw, team) float64 ...
      defs_star     (chain, draw, team) float64 ...
      away_points   (chain, draw, match) int64 ...
      sd_att        (chain, draw) float64 ...
      sd_def_log__  (chain, draw) float64 ...
      home          (chain, draw) float64 ...
      atts          (chain, draw, team) float64 ...
      sd_def        (chain, draw) float64 ...
      home_points   (chain, draw, match) int64 ...
      defs          (chain, draw, team) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.573731
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • chain: 1
    • draw: 500
    • team: 6
    • match: 60
  • Coordinates: (4)
    • chain
      (chain)
      int64
      0

      array([0])

    • draw
      (draw)
      int64
      0 1 2 3 4 5 ... 495 496 497 498 499

      array([  0,   1,   2, ..., 497, 498, 499])

    • team
      (team)
      object
      'Wales' 'France' ... 'England'

      array(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'],
            dtype=object)

    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

  • Data variables: (12)
    • sd_att_log__
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • intercept
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • atts_star
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • defs_star
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • away_points
      (chain, draw, match)
      int64
      ...

      [30000 values with dtype=int64]

    • sd_att
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • sd_def_log__
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • home
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • atts
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

    • sd_def
      (chain, draw)
      float64
      ...

      [500 values with dtype=float64]

    • home_points
      (chain, draw, match)
      int64
      ...

      [30000 values with dtype=int64]

    • defs
      (chain, draw, team)
      float64
      ...

      [3000 values with dtype=float64]

  • Indexes: (4)
    • chain
      PandasIndex

      PandasIndex(Index([0], dtype='int64', name='chain'))

    • draw
      PandasIndex

      PandasIndex(Index([  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,
             ...
             490, 491, 492, 493, 494, 495, 496, 497, 498, 499],
            dtype='int64', name='draw', length=500))

    • team
      PandasIndex

      PandasIndex(Index(['Wales', 'France', 'Ireland', 'Scotland', 'Italy', 'England'], dtype='object', name='team'))

    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.573731

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  
• observed_data

  <xarray.Dataset>
  Dimensions:      (match: 60)
  Coordinates:
    * match        (match) object 'Wales Italy' ... 'Ireland England'
  Data variables:
      home_points  (match) float64 ...
      away_points  (match) float64 ...
  Attributes:
      created_at:                 2019-07-12T20:31:53.581293
      inference_library:          pymc3
      inference_library_version:  3.7

  xarray.Dataset

  • Dimensions:
    • match: 60
  • Coordinates: (1)
    • match
      (match)
      object
      'Wales Italy' ... 'Ireland England'

      array(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'], dtype=object)

  • Data variables: (2)
    • home_points
      (match)
      float64
      ...

      [60 values with dtype=float64]

    • away_points
      (match)
      float64
      ...

      [60 values with dtype=float64]

  • Indexes: (1)
    • match
      PandasIndex

      PandasIndex(Index(['Wales Italy', 'France England', 'Ireland Scotland', 'Ireland Wales',
             'Scotland England', 'France Italy', 'Wales France', 'Italy Scotland',
             'England Ireland', 'Ireland Italy', 'Scotland France', 'England Wales',
             'Italy England', 'Wales Scotland', 'France Ireland', 'Wales England',
             'Italy Ireland', 'France Scotland', 'England Italy', 'Ireland France',
             'Scotland Wales', 'Scotland Italy', 'France Wales', 'Ireland England',
             'Wales Ireland', 'England Scotland', 'Italy France', 'Italy Wales',
             'Scotland Ireland', 'England France', 'France Italy',
             'Scotland England', 'Ireland Wales', 'France Ireland', 'Wales Scotland',
             'Italy England', 'Wales France', 'Italy Scotland', 'England Ireland',
             'Ireland Italy', 'England Wales', 'Scotland France', 'Wales Italy',
             'Ireland Scotland', 'France England', 'Scotland Ireland',
             'England France', 'Italy Wales', 'Italy Ireland', 'Wales England',
             'France Scotland', 'Scotland Wales', 'Ireland France', 'England Italy',
             'Wales Ireland', 'Italy France', 'England Scotland', 'Scotland Italy',
             'France Wales', 'Ireland England'],
            dtype='object', name='match'))

  • Attributes: (3)

    created_at :

    2019-07-12T20:31:53.581293

    inference_library :

    pymc3

    inference_library_version :

    3.7

  
  

Note that in the first example we have used the kwargs argument and in the second we have used the group_dict one.