Multinomial probit Bayesian additive regression trees
Copyright policy )Multinomial probit Bayesian additive regression trees
This article proposes multinomial probit Bayesian additive regression trees (MPBART) as a multinomial probit extension of Bayesian additive regression trees. MPBART is flexible to allow inclusion of predictors that describe the observed units as well as the available choice alternatives. Through two simulation studies and four real data examples, we show that MPBART exhibits very good predictive performance in comparison with other discrete choice and multiclass classification methods. To implement MPBART, theRpackagempbartis freely available from CRAN repositories. Copyright © 2016 John Wiley & Sons, Ltd.
- University of South Carolina United States
- University of South Carolina System United States
- Michigan State University United States
machine learning, Bayesian methods, classification, Statistics, statistical computing
machine learning, Bayesian methods, classification, Statistics, statistical computing
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