Original author(s) | ArviZ Development Team |
---|---|
Initial release | July 21, 2018 |
Stable release | 0.18.0[1]
/ 4 April 2024 |
Repository | https://github.com/arviz-devs/arviz |
Written in | Python |
Operating system | Unix-like, Mac OS X, Microsoft Windows |
Platform | Intel x86 – 32-bit, x64 |
Type | Statistical package |
License | Apache License, Version 2.0 |
Website | python |
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:
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]