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.
翻译:近年来,贝叶斯统计方法在许多研究领域和工业应用领域得到的普及程度急剧提高,这是因为在方法上取得了各种进步,采用了更快、更廉价的硬件,并开发了新的软件工具。在这里,我们引入了一个名为Bambi(Bayesian模型建模界面)的开放源代码Python软件包,该软件包建在PyMC概率性编程框架和ArviZ模型套件之上,用于对巴伊西亚模型进行探索性分析。Bambi使得使用类似于R的公式符号来说明复杂的一般线性线性等级模式变得容易。我们用几个例子展示了Bambi的多功能和使用方便性,这些例子涉及一系列共同的统计模型,包括多重回归、物流回归和具有跨群体特定效应的混合效应模型。此外,我们讨论了如何建立自动的前程。最后,我们讨论了我们今后开发Bambi的计划。