Skip to content
Switch branches/tags

Latest commit

* added space ` plot_posterior :` and `Plot `  and ,  `plot_density :` and `Plot`

* Added empty space line before the examples

* Resolved

* space between line

* Added space between object and or

* include a semi-open group. so the range is (0,1]

* removed repeated lines

* removed repeated line

* removed repeated line

* Added  refrecences , see also section , backend_kwargs

* Edited the long line

* removing the changes of other two file except forestplot in this pr

* Added final new line in densityplot

* removing the change made in other two except forestPlot

* edited the some lines

* Update

* added refernces ,see also section and backend kwarg.

* removed trailing whitespaces and sorted long line

* edited some lines

* added suggested changes

* added suggested changes

* added suggested changes 3

* added references, see also, backend kwarg

* added added dashed line in see also

* Apply suggestions from code review

Co-authored-by: Rosheen Naeem <>

* added arviz ref. in next line

* removing trailing spaces and long line

* Apply suggestions from code review

Co-authored-by: Rosheen Naeem <>

* edited long line

* Apply suggestions from code review

Co-authored-by: Rosheen Naeem <>

* added new functions in see also section.

Co-authored-by: Rosheen Naeem <>

Git stats


Failed to load latest commit information.

PyPI version 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, data storage, 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 main 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


ArviZ is tested on Python 3.6, 3.7 and 3.8, and depends on NumPy, SciPy, xarray, and Matplotlib.


If you use ArviZ and want to cite it please use DOI

Here is the citation in BibTeX format

  doi = {10.21105/joss.01143},
  url = {},
  year = {2019},
  publisher = {The Open Journal},
  volume = {4},
  number = {33},
  pages = {1143},
  author = {Ravin Kumar and Colin Carroll and Ari Hartikainen and Osvaldo Martin},
  title = {ArviZ a unified library for exploratory analysis of Bayesian models in Python},
  journal = {Journal of Open Source Software}


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


ArviZ is a non-profit project under NumFOCUS umbrella. If you want to support ArviZ financially, you can donate here.