ArviZ explained

ArviZ
Author:	ArviZ Development Team
Repo:	https://github.com/arviz-devs/arviz
Programming Language:	Python
Operating System:	Unix-like, Mac OS X, Microsoft Windows
Platform:	Intel x86 – 32-bit, x64
Genre:	Statistical package
License:	Apache License, Version 2.0

ArviZ is a Python package for exploratory analysis of Bayesian models.^{[1] [2] [3] [4]} 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.^[5] and it has been used to help interpret inference problems in several scientific domains, including astronomy,^[6] neuroscience,^[7] physics^[8] and statistics.^{[9] [10]}

Etymology

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.

Exploratory analysis of Bayesian models

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.^{[11] [12] [13]}

Library features

• InferenceData object for Bayesian data manipulation. This object is based on xarray
• Plots using two alternative backends matplotlib or bokeh
• Numerical summaries and diagnostics for Markov chain Monte Carlo methods.
• Integration with established probabilistic programming languages including; PyStan (the Python interface of Stan), PyMC,^[14] Edward^[15] Pyro,^[16] and easily integrated with novel or bespoke Bayesian analyses. ArviZ is also available in Julia, using the ArviZ.jl interface

External links

• ArviZ web site

Notes and References

1. ArviZ a unified library for exploratory analysis of Bayesian models in Python . 2019 . Kumar . Ravin . Carroll . Colin . Hartikainen . Ari . Martin . Osvaldo . Journal of Open Source Software . 4 . 33 . 1143 . 2019JOSS....4.1143K . 10.21105/joss.01143 . free . 11336/114615 . free .
2. Book: Bayesian Analysis with Python - Third Edition: A practical guide to probabilistic modeling. Martin. Osvaldo. 2024. Packt Publishing Ltd. 9781805127161. en.
3. Book: Bayesian Modeling and Computation in Python. Martin. Osvaldo. Kumar. Ravin. Lao. Junpeng. 2021. CRC-press. 9780367894368. 1–420. en. 7 July 2022.
4. Bayesian Workflow . 2021 . Gelman . Andrew . Vehtari . Aki . Simpson . Daniel . Margossian . Charles . Carpenter . Bob . Yao . Yuling . Kennedy . Lauren . Gabry . Jonah . Bürkner . Paul-Christian . Martin . Modrák . stat.ME . 2011.01808.
5. News: NumFOCUS Affiliated Projects. NumFOCUS Open Code = Better Science. 2019-11-30.
6. 10.3847/2041-8213/ab4284 . A Future Percent-level Measurement of the Hubble Expansion at Redshift 0.8 with Advanced LIGO . 2019 . Farr . Will M. . Fishbach . Maya . Ye . Jiani . Holz . Daniel E. . 202150341 . The Astrophysical Journal . 883 . 2 . L42 . 1908.09084 . 2019ApJ...883L..42F . free .
7. 10.1111/ejn.15470 . Trait anxiety effects on late phase threatening speech processing: Evidence from electroencephalography . 2021 . Busch-Moreno . Simon . Tuomainen . Jyrki . Vinson . David . European Journal of Neuroscience . 54 . 9 . 7152–7175 . 34553432 . free .
8. 10.1063/1.5120503 . Bayesian consensus clustering in multiplex networks . 2019 . Jovanovski . Petar . Kocarev . Ljupco . Chaos: An Interdisciplinary Journal of Nonlinear Science . 29 . 10 . 103142 . 31675792 . 2019Chaos..29j3142J . 207834500 .
9. 1909.04852 . Zhou . Guangyao . Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables . 2019 . stat.CO .
10. 1912.02982. Graham. Matthew M.. Thiery. Alexandre H.. Beskos. Alexandros. Manifold Markov chain Monte Carlo methods for Bayesian inference in a wide class of diffusion models. 2019. stat.CO .
11. 1709.01449 . 10.1111/rssa.12378 . Visualization in Bayesian workflow . 2019 . Gabry . Jonah . Simpson . Daniel . Vehtari . Aki . Betancourt . Michael . Gelman . Andrew . 26590874 . Journal of the Royal Statistical Society, Series A (Statistics in Society) . 182 . 2 . 389–402 .
12. 1903.08008 . Vehtari . Aki . Gelman . Andrew . Simpson . Daniel . Carpenter . Bob . Bürkner . Paul-Christian . Rank-Normalization, Folding, and Localization: An Improved Rˆ for Assessing Convergence of MCMC (With Discussion) . Bayesian Analysis . 2021 . 16 . 2 . 667 . 10.1214/20-BA1221 . 2021BayAn..16..667V . 88522683 .
13. Book: Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ. Martin. Osvaldo. 2018. Packt Publishing Ltd. 9781789341652. en.
14. 10.7717/peerj-cs.55 . Probabilistic programming in Python using PyMC3 . 2016 . Salvatier . John . Wiecki . Thomas V. . Fonnesbeck . Christopher . PeerJ Computer Science . 2 . e55 . free . 1507.08050 .
15. 1610.09787 . Tran . Dustin . Kucukelbir . Alp . Dieng . Adji B. . Rudolph . Maja . Liang . Dawen . Blei . David M. . Edward: A library for probabilistic modeling, inference, and criticism . 2016 . stat.CO .
16. 1810.09538 . Bingham . Eli . Chen . Jonathan P. . Jankowiak . Martin . Obermeyer . Fritz . Pradhan . Neeraj . Karaletsos . Theofanis . Singh . Rohit . Szerlip . Paul . Horsfall . Paul . Goodman . Noah D. . Pyro: Deep Universal Probabilistic Programming . 2018 . cs.LG .