<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nobile, Agostino</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A hybrid Markov chain for the Bayesian analysis of the multinomial probit model</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics and Computing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Gibbs sampling</style></keyword><keyword><style  face="normal" font="default" size="100%">Metropolis algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Multinomial probit model</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1023/A%3A1008905311214</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers</style></publisher><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">229-242</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;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.&lt;/p&gt;
</style></abstract></record></records></xml>