  This article proposes Multinomial Probit Bayesian Additive Regression Trees
(MPBART) as a multinomial probit extension of BART - Bayesian Additive
Regression Trees (Chipman et al (2010)). 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 to
other discrete choice and multiclass classification methods. To implement
MPBART, we have developed an R package mpbart available freely from CRAN
repositories.
