bbl: Boltzmann Bayes Learner for High-Dimensional Inference with Discrete Predictors in R

Jun Woo, Jinhua Wang

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1-32
Number of pages32
JournalJournal of Statistical Software
Issue number5
StatePublished - 2022

Bibliographical note

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© 2022, American Statistical Association. All rights reserved.


  • Boltzmann machine
  • Discriminant analysis
  • Naive Bayes
  • R
  • Supervised learning


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