ArviZ (/ˈɑːrvɪz/AR-vees) is a Python package for exploratory analysis of Bayesian models.[2][3][4][5] It is specifically designed to work with the output of probabilistic programming libraries like PyMC, Stan, and others by providing a set of tools for summarizing and visualizing the results of Bayesian inference in a convenient and informative way. ArviZ also provides a common data structure for manipulating and storing data commonly arising in Bayesian analysis, like posterior samples or observed data.
ArviZ is an open source project, developed by the community and is an affiliated project of NumFOCUS.[6] and it has been used to help interpret inference problems in several scientific domains, including astronomy,[7] neuroscience,[8] physics[9] and statistics.[10][11]
The ArviZ name is derived from reading "rvs" (the short form of random variates) as a word instead of spelling it and also using the particle "viz" usually used to abbreviate visualization.
When working with Bayesian models there are a series of related tasks that need to be addressed besides inference itself:
Diagnoses of the quality of the inference, this is needed when using numerical methods such as Markov chain Monte Carlo techniques
Model criticism, including evaluations of both model assumptions and model predictions
Comparison of models, including model selection or model averaging
Preparation of the results for a particular audience
All these tasks are part of the Exploratory analysis of Bayesian models approach, and successfully performing them is central to the iterative and interactive modeling process. These tasks require both numerical and visual summaries.[12][13][14]
Integration with established probabilistic programming languages including; PyStan (the Python interface of Stan), PyMC,[15] Edward[16] Pyro,[17] and easily integrated with novel or bespoke Bayesian analyses. ArviZ is also available in Julia, using the ArviZ.jl interface
^Zhou, Guangyao (2019). "Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables". arXiv:1909.04852 [stat.CO].
^Graham, Matthew M.; Thiery, Alexandre H.; Beskos, Alexandros (2019). "Manifold Markov chain Monte Carlo methods for Bayesian inference in a wide class of diffusion models". arXiv:1912.02982 [stat.CO].
^Gabry, Jonah; Simpson, Daniel; Vehtari, Aki; Betancourt, Michael; Gelman, Andrew (2019). "Visualization in Bayesian workflow". Journal of the Royal Statistical Society, Series A (Statistics in Society). 182 (2): 389–402. arXiv:1709.01449. doi:10.1111/rssa.12378. S2CID26590874.
^Vehtari, Aki; Gelman, Andrew; Simpson, Daniel; Carpenter, Bob; Bürkner, Paul-Christian (2021). "Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (With Discussion)". Bayesian Analysis. 16 (2): 667. arXiv:1903.08008. Bibcode:2021BayAn..16..667V. doi:10.1214/20-BA1221. S2CID88522683.
