ArviZ: Exploratory analysis of Bayesian models#


Exploratory analysis of Bayesian models

ArviZ is a Python package for exploratory analysis of Bayesian models. It serves as a backend-agnostic tool for diagnosing and visualizing Bayesian inference.

Key Features#


Integrates with all major probabilistic programming libraries: PyMC, CmdStanPy, PyStan, Pyro, NumPyro, and emcee.


Large Suite of Visualizations
Provides over 25 plotting functions for all parts of Bayesian workflow: visualizing distributions, diagnostics, and model checking. See the gallery for examples.


State of the Art Diagnostics
Latest published diagnostics and statistics are implemented, tested and distributed with ArviZ.


Flexible Model Comparison
Includes functions for comparing models with information criteria, and cross validation (both approximate and brute force).


Built for Collaboration
Designed for flexible cross-language serialization using netCDF or Zarr formats. ArviZ also has a Julia version that uses the same data schema.


Labeled Data
Builds on top of xarray to work with labeled dimensions and coordinates.

Sponsors and Institutional Partners#

We thank these institutions for generously supporting the development and maintenance of ArviZ.




Support ArviZ#


Contributions and issue reports are very welcome at the GitHub repository. We have a contributing guide to help you through the process. If you have any doubts, please do not hesitate to contact us on gitter.


If you use ArviZ, please cite it using JOSS.

See our support page for information on how to cite in BibTeX format.

Become a Sponsor

If your company or institution uses ArviZ, we encourage you to make a donation to ArviZ or to allow employees to dedicate some of their time to ArviZ.

See Details

Shop ArviZ Merchandise




ArviZ is a non-profit project under the NumFOCUS umbrella. To support ArviZ financially, consider donating through the NumFOCUS website.