MARTIN Osvaldo Antonio
Bambi: A simple interface for fitting Bayesian linear models in Python
TOMÁS CAPRETTO; CAMEN PIHO; RAVIN KUMAR; JACOB WESTFALL; TAL YARKONI; MARTÍN OSVALDO A.
JOURNAL OF STATISTICAL SOFTWARE
JOURNAL STATISTICAL SOFTWARE
Año: 2022 vol. 103 p. 1 - 29
The popularity of Bayesian statistical methods has increased dramatically inrecent years across many research areas and industrial applications. This isthe result of a variety of methodological advances with faster and cheaperhardware as well as the development of new software tools. Here we introduce anopen source Python package named Bambi (BAyesian Model Building Interface) thatis built on top of the PyMC probabilistic programming framework and the ArviZpackage for exploratory analysis of Bayesian models. Bambi makes it easy tospecify complex generalized linear hierarchical models using a formula notationsimilar to those found in R. We demonstrate Bambi´s versatility and ease of usewith a few examples spanning a range of common statistical models includingmultiple regression, logistic regression, and mixed-effects modeling withcrossed group specific effects. Additionally we discuss how automatic priorsare constructed. Finally, we conclude with a discussion of our plans for thefuture development of Bambi.