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  • Getting Started
  • Example Gallery
  • User Guide
  • API
  • Community
  • Contributing
  • The ArviZ project
  • GitHub
  • Bluesky
  • Mastodon

Section Navigation

  • Installation guide
  • ArviZ Quickstart
  • Introduction to xarray, InferenceData, and netCDF for ArviZ
  • Creating InferenceData
    • Converting emcee objects to InferenceData
  • Working with InferenceData
  • Getting Started

Getting Started#

  • Installation guide
    • Stable
    • Development
    • Dependencies
  • ArviZ Quickstart
    • ArviZ style sheets
    • Get started with plotting
    • ArviZ rcParams
    • PyMC integration
    • CmdStanPy integration
  • Introduction to xarray, InferenceData, and netCDF for ArviZ
    • Why more than one data structure?
    • Why not Pandas Dataframes or NumPy Arrays?
    • An introduction to each
    • NetCDF
  • Additional Reading
    • InferenceData
    • xarray
    • NetCDF
  • Creating InferenceData
    • From 1D numpy array
    • From nD numpy array
    • From a dictionary
    • From dictionary with coords and dims
    • From Dataframe
    • From PyMC3
    • From PyStan
    • From Pyro
    • From emcee
    • From CmdStanPy
    • From CmdStan
    • From NumPyro
    • From PyJAGS
  • Working with InferenceData
    • Get the dataset corresponding to a single group
    • Add a new variable
    • Combine chains and draws
    • Get a random subset of the samples
    • Obtain a NumPy array for a given parameter
    • Get the dimension lengths
    • Get coordinate values
    • Get a subset of chains
    • Remove the first n draws (burn-in)
    • Compute posterior mean values along draw and chain dimensions
    • Compute and store posterior pushforward quantities
    • Advanced subsetting
    • Add new chains using concat
    • Add groups to InferenceData objects
    • Add Transformations to Multiple Groups

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