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B0677
Title: A class of conjugate priors for multinomial probit models which includes the multivariate normal one Authors:  Augusto Fasano - Bocconi University (Italy) [presenting]
Daniele Durante - Bocconi University (Italy)
Abstract: Multinomial probit models are widely-implemented representations which allow both classification and inference by learning changes in vectors of class probabilities with a set of p observed predictors. Although various frequentist methods have been developed for estimation, inference and classification within such a class of models, Bayesian inference is still lagging. This is due to the apparent absence of a tractable class of conjugate priors, that may facilitate posterior inference on the multinomial probit coefficients. Such an issue has motivated increasing efforts toward the development of effective Markov chain Monte Carlo methods. However, state-of-the-art solutions still face severe computational bottlenecks, especially in large p settings. We prove that the entire class of unified skew-normal (SUN) distributions is conjugate to a wide variety of multinomial probit models. We exploit the SUN properties to improve upon state-of-the-art-solutions for posterior inference and classification both in terms of closed-form results for key functionals of interest, and also by developing novel computational methods relying either on i.i.d. samples from the exact posterior or on scalable and accurate variational approximations based on blocked partially-factorized representations. As illustrated in a gastrointestinal lesions application, the magnitude of the improvements relative to current methods is particularly evident when the focus is on large p applications.