Azure Build Status codecov Code style: black Gitter chat DOI DOI Powered by NumFOCUS


ArviZ (pronounced "AR-vees") is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, model checking, comparison and diagnostics.

ArviZ in other languages

ArviZ also has a Julia wrapper available ArviZ.jl.


The ArviZ documentation can be found in the official docs. First time users may find the quickstart to be helpful. Additional guidance can be found in the usage documentation.



ArviZ is available for installation from PyPI. The latest stable version can be installed using pip:

pip install arviz

ArviZ is also available through conda-forge.

conda install -c conda-forge arviz


The latest development version can be installed from the master branch using pip:

pip install git+git://

Another option is to clone the repository and install using git and setuptools:

git clone
cd arviz
python install


Ridge plot Parallel plot Trace plot Density plot
Posterior plot Joint plot Posterior predictive plot Pair plot
Energy Plot Violin Plot Forest Plot Autocorrelation Plot
## Dependencies ArviZ is tested on Python 3.6, 3.7 and 3.8, and depends on NumPy, SciPy, xarray, and Matplotlib. ## Citation If you use ArviZ and want to cite it please use [![DOI](]( Here is the citation in BibTeX format ``` @article{arviz_2019, title = {{ArviZ} a unified library for exploratory analysis of {Bayesian} models in {Python}}, author = {Kumar, Ravin and Colin, Carroll and Hartikainen, Ari and Martin, Osvaldo A.}, journal = {The Journal of Open Source Software}, year = {2019}, doi = {10.21105/joss.01143}, url = {}, } ``` ## Contributions ArviZ is a community project and welcomes contributions. Additional information can be found in the [Contributing Readme]( ## Code of Conduct ArviZ wishes to maintain a positive community. Additional details can be found in the [Code of Conduct]( ## Sponsors [![NumFOCUS](](