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

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2022-08-15
Major/Subject
Mcode
Degree programme
Language
en
Pages
29
Series
JOURNAL OF STATISTICAL SOFTWARE, Volume 103, issue 15
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.
Description
Keywords
Bayesian statistics, generalized linear models, multilevel modeling, python, hierarchical Bayesian modeling
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Citation
Capretto , T , Piho , C , Kumar , R , Westfall , J , Yarkoni , T & Martin , O 2022 , ' Bambi: A simple interface for fitting Bayesian linear models in Python ' , JOURNAL OF STATISTICAL SOFTWARE , vol. 103 , no. 15 . https://doi.org/10.18637/jss.v103.i15