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 efficiently 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 efficiency and flexibility of the method. (C) 2002 Elsevier Science B.V. All rights reserved.