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

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

: Bayesian statistics, generalized linear models, multilevel models, hierarchical models, mixed effect models, 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.

Tomás Capretto

https://www.jstatsoft.org/article/view/v103i15

University of Texas at Austin

July 2022

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