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

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.

Automated summary

Bayesian statistical methods have become increasingly popular in recent years due to methodological advances, faster and cheaper hardware, and the development of new software tools. Bambi is an open source Python package built on top of PyMC and ArviZ that makes it easy to specify complex generalized linear hierarchical models, and it showcases its versatility and ease of use with various examples.