Bambi: A simple interface for fitting Bayesian linear models in Python

TL;DR

Bambi is an open source Python package that simplifies the specification and fitting of Bayesian linear and generalized linear models, making Bayesian methods more accessible.

Contribution

It introduces a user-friendly interface for Bayesian modeling in Python, built on PyMC, with automatic prior construction and support for complex hierarchical models.

Findings

Bambi enables easy specification of various Bayesian models.

It demonstrates versatility across multiple statistical modeling scenarios.

The package simplifies Bayesian analysis for researchers and practitioners.

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.