Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python
Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python
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DOI：
10.18637/jss.v103.i15
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发表时间：
2022-08-01
影响因子：
5.8
通讯作者：
Martin, Osvaldo A.
中科院分区：
文献类型：
--
作者：
Capretto, Tomas;Piho, Camen;Martin, Osvaldo A.
近年来，贝叶斯统计方法在许多研究领域和工业应用中的普及程度急剧增加。这是各种方法进步、更快、更便宜的硬件以及新软件工具开发的结果。在这里，我们介绍了一个名为Bambi（贝叶斯模型构建接口）的开源Python包，它构建在PyMC概率编程框架和ArviZ包之上，用于贝叶斯模型的探索性分析。Bambi使用类似于R中的公式符号，使指定复杂的广义线性层次模型变得容易。我们展示一下小鹿斑比？的多功能性和易用性与几个例子跨越了一系列常见的统计模型，包括多元回归，逻辑回归，混合效应建模与交叉组的具体影响。此外，我们还讨论了自动先验是如何构建的。最后，我们总结了我们对Bambi未来发展的计划。
The popularity of Bayesian statistical methods has increased dramatically in recent years across many research areas and industrial applications. This is the result of a variety of methodological advances with faster and cheaper hardware as well as the development of new software tools. Here we introduce an open source Python package named Bambi (BAyesian Model Building Interface) that is built on top of the PyMC probabilistic programming framework and the ArviZ package for exploratory analysis of Bayesian models. Bambi makes it easy to specify complex generalized linear hierarchical models using a formula notation similar to those found in R. We demonstrate Bambi???s versatility and ease of use with a few examples spanning a range of common statistical models including multiple regression, logistic regression, and mixed-effects modeling with crossed group specific effects. Additionally we discuss how automatic priors are constructed. Finally, we conclude with a discussion of our plans for the future development of Bambi.