Bayesian analysis of multivariate nominal measures using multivariate multinomial probit models
Computational Statistics & Data Analysis
Fast simulation of truncated Gaussian distributions
Statistics and Computing
Data augmentation strategies for the Bayesian spatial probit regression model
Computational Statistics & Data Analysis
Bayesian Learning of Noisy Markov Decision Processes
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
Sequential Monte Carlo EM for multivariate probit models
Computational Statistics & Data Analysis
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Bayesian inference for the multinomial probit model, using the Gibbs sampler with data augmentation, has been recently considered by some authors. The present paper introduces a modification of the sampling technique, by defining a hybrid Markov chain in which, after each Gibbs sampling cycle, a Metropolis step is carried out along a direction of constant likelihood. Examples with simulated data sets motivate and illustrate the new technique. A proof of the ergodicity of the hybrid Markov chain is also given.