Bambi: A Simple Interface for Fitting Bayesian Linear Models in Python

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Tom'as Capretto, Camen Piho, Ravi Kumar, Jacob Westfall, T. Yarkoni, O. A. Martin
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引用次数: 40

Abstract

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.
Bambi:一个用Python拟合贝叶斯线性模型的简单接口
近年来,贝叶斯统计方法在许多研究领域和工业应用中得到了极大的普及。这是各种方法进步的结果,伴随着更快、更便宜的硬件以及新软件工具的开发。在这里,我们介绍一个名为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包的基础上,用于对贝叶斯模型进行探索性分析。Bambi使用类似于r中的公式符号可以很容易地指定复杂的广义线性层次模型。我们通过几个示例展示了Bambi的多功能性和易用性,这些示例涵盖了一系列常见的统计模型,包括多元回归、逻辑回归和具有跨组特定效果的混合效应建模。此外,我们还讨论了如何构造自动先验。最后,我们将讨论小鹿斑比未来的发展计划。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Statistical Software
Journal of Statistical Software 工程技术-计算机:跨学科应用
CiteScore
10.70
自引率
1.70%
发文量
40
审稿时长
6-12 weeks
期刊介绍: The Journal of Statistical Software (JSS) publishes open-source software and corresponding reproducible articles discussing all aspects of the design, implementation, documentation, application, evaluation, comparison, maintainance and distribution of software dedicated to improvement of state-of-the-art in statistical computing in all areas of empirical research. Open-source code and articles are jointly reviewed and published in this journal and should be accessible to a broad community of practitioners, teachers, and researchers in the field of statistics.
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