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.

%B Statistics and Computing %I Kluwer Academic Publishers %V 8 %P 229-242 %G eng %U http://dx.doi.org/10.1023/A%3A1008905311214 %R 10.1023/A:1008905311214