arviz-devs/arviz：使用Python探索性分析Bayesian模型

arviz-devs

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Apache License 2.0

项目介绍

arviz-devs/arviz 是一个托管在 GitHub 上的开源项目，主要使用 TeX 编写，采用 Apache License 2.0 许可证，目前获得 1.9k 个 Star、511 次 Fork。

仓库全名: arviz-devs/arviz
开源许可证: Apache License 2.0
Star 数: 1.9k
Fork 数: 511
未关闭 Issue: 108

README 摘要

ArviZ (pronounced "AR-vees") is a Python package for exploratory analysis of Bayesian models. It 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. Documentation The ArviZ documentation can be found in the official docs. Here are some quick links for common scenarios: First time Bayesian…

完整内容见 GitHub 原始 README 或本页。

常见问题

arviz 用什么语言开发？

主要使用 TeX 编写，采用 Apache License 2.0 许可证，目前有 1.9k 个 Star。

arviz 可以免费商用吗？

如何获取 arviz 的源码？

可以直接克隆仓库：git clone https://github.com/arviz-devs/arviz.git，默认分支为 main；安装与运行步骤见仓库 README。

arviz 最近还在维护吗？

仓库最近一次更新为 3 天前，当前有 108 个未关闭 Issue；Star 数为 1.9k，可作为社区活跃度的参考。

关于

GitHub 原始描述（英文）

Exploratory analysis of Bayesian models with Python

描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

2935

价值分

1271

活跃状态

活跃

bayesian closember python

GitHub

语言占比

Python

TeX

Release

v1.3.08月11日
v1.2.06月13日
v1.1.04月24日
v1.0.03月2日
v0.23.42月5日

贡献者

README

ArviZ (pronounced “AR-vees”) is a Python package for exploratory analysis of Bayesian models. It 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.

Documentation

The ArviZ documentation can be found in the official docs. Here are some quick links for common scenarios:

First time Bayesian modelers and ArviZ users: EABM book
First time ArviZ users, already familiar with Bayesian modeling: overview notebook or example gallery
ArviZ 0.x user: migration guide
ArviZ-verse documentation:

Installation

Stable

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

pip install "arviz[preview]"

ArviZ is also available through conda-forge.

conda install -c conda-forge arviz arviz-plots

Development

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

pip install git+https://github.com/arviz-devs/arviz.git

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

git clone https://github.com/arviz-devs/arviz.git
cd arviz
python setup.py install

Gallery

And more…

Citation

If you use ArviZ and want to cite it please use

Here is the citation in BibTeX format

@article{Martin2026,
doi = {10.21105/joss.09889},
url = {https://doi.org/10.21105/joss.09889},
year = {2026},
publisher = {The Open Journal},
volume = {11},
number = {119},
pages = {9889},
author = {Martin, Osvaldo A. and Abril-Pla, Oriol and Deklerk, Jordan and Axen, Seth D. and Carroll, Colin and Hartikainen, Ari and Vehtari, Aki},
title = {ArviZ: a modular and flexible library for exploratory analysis of Bayesian models},
journal = {Journal of Open Source Software}}

Contributions

ArviZ is a community project and welcomes contributions. Additional information can be found in the contributing guide

Code of Conduct

ArviZ wishes to maintain a positive community. Additional details can be found in the Code of Conduct

Donations

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

Sponsors and Institutional Partners

The ArviZ project website has more information about each sponsor and institutional partner and the support they provide.

Institutional partners

ArviZ is currently supported by the following institutional partners:

arviz