A Bayesian model selection method with applications

In this paper, we consider Bayesian model selection using the well-known Bayes factor. A method on the basis of path sampling for computing the ratio of two normalizing constants involved in the Bayes factor is proposed. The key idea is to construct a continuous path to link up the competing models, then the Bayes factor can be estimated e6ciently by means of grids in [0,1] and observations simulated from the posterior distribution of the parameters. This method is applied to non-nested regression models, mixture models with an unknown number of components, and a general latent variable model with mixed continuous and polytomous variables. Analyses of some real data sets are presented to illustrate the e6ciency and :exibility of the method. c © 2002 Elsevier Science B.V. All rights reserved.