Bayesian nonparametric mixed random utility models

Wepropose amixedmultinomial logit model, with themixing distribution assigned a general (nonparametric) stick-breaking prior.Wepresent aMarkov chainMonte Carlo (MCMC) algorithm to sample and estimate the posterior distribution of the model’s parameters. The algorithm relies on a Gibbs (slice) sampler that is useful for Bayesian nonparametric (infinite-dimensional) models. The model and algorithm are illustrated through the analysis of real data involving 10 choice alternatives, and we prove the posterior consistency of the model. Published by Elsevier B.V.