BAYESIAN CART MODEL SEARCH

In this article we put forward a Bayesian approach for finding classif ication and regression tree (CART) models. The two basic components of this approach consist of prior specification and stochastic search. T he basic idea is to have the prior induce a posterior distribution tha t will guide the stochastic search toward more promising CART models. As the search proceeds, such models can then be selected with a variet y of criteria, such as posterior probability, marginal likelihood, res idual sum of squares or misclassification rates. Examples are used to illustrate the potential superiority of this approach over alternative methods.