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