bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R
Jun Woo, Jinhua Wang

bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R
Jun Woo, Jinhua Wang

supervised learning, Boltzmann machine, naive Bayes, discriminant analysis, R.

Non-regression-based inferences, such as discriminant analysis, can account for the
effect of predictor distributions that may be significant in big data modeling. We describe bbl, an R package for Boltzmann Bayes learning, which enables a comprehensive
supervised learning of the association between a large number of categorical predictors
and multi-level response variables. Its basic underlying statistical model is a collection of
(fully visible) Boltzmann machines inferred for each distinct response level. The algorithm
reduces to the naive Bayes learner when interaction is ignored. We illustrate example use
cases for various scenarios, ranging from modeling of a relatively small set of factors with
heterogeneous levels to those with hundreds or more predictors with uniform levels such
as image or genomic data. We show how bbl explicitly quantifies the extra power provided
by interactions via higher predictive performance of the model. In comparison to deep
learning-based methods such as restricted Boltzmann machines, bbl-trained models can
be interpreted directly via their bias and interaction parameters.

Jun Woo

https://www.jstatsoft.org/article/view/v101i05

University of Minnesota, Minneapolis

January 2022

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