A hybrid Markov chain for the Bayesian analysis of the multinomial probit model

作     者：Nobile, A 

作者机构：Univ Bristol Dept Math Bristol BS8 1TW Avon England 

出 版 物：《STATISTICS AND COMPUTING》 (统计学与计算)

年 卷 期：1998年第8卷第3期

页      面：229-242页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Science Foundation  NSF  (DMS9208758  DMS9313013) 

主　　题：multinomial probit model Gibbs sampling Metropolis algorithm Bayesian analysis 

摘      要：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.