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A Bayesian analysis of the multinomial probit model using marginal data augmentation Imai, Kosuke van Dyk, David A. Uploaded 08-21-2002 Keywords Bayesian analysis Data augmentation Prior distributions Probit models Rate of convergence Abstract We introduce a set of new Markov chain Monte Carlo algorithms for Bayesian analysis of the multinomial probit model. Our Bayesian representation of the model places a new, and possibly improper, prior distribution directly on the identifiable parameters and thus is relatively easy to interpret and use. Our algorithms, which are based on the method of marginal data augmentation, involve only draws from standard distributions and dominate other available Bayesian methods in that they are as quick to converge as the fastest methods but with a more attractive prior specification.