论文标题
小鹿斑比：一个简单的界面，用于拟合Python中的贝叶斯线性模型
Bambi: A simple interface for fitting Bayesian linear models in Python
论文作者
论文摘要
近年来，在许多研究领域和工业应用中，贝叶斯统计方法的普及急剧增加。这是通过多种方法学进步以及更便宜的硬件以及新软件工具的开发的结果。在这里，我们介绍了一个名为Bambi（贝叶斯模型建筑接口）的开源Python软件包，该软件包构建在PYMC Probabilistic编程框架和Arviz软件包的顶部，用于贝叶斯模型的探索性分析。 BAMBI使使用与R相似的公式表示法指定复杂的广义线性层次模型，从而表明BAMBI的多功能性和易用性，其中一些涵盖了一系列常见统计模型，包括多重回归，逻辑回归，以及具有跨组特定效果的混合效应模型。此外，我们讨论了如何构建自动先验。最后，我们在讨论了小鹿斑比未来发展的计划中进行了讨论。
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.
