Bayesian CART Model Search
提出一种贝叶斯方法，通过先验设定和随机搜索来寻找分类与回归树模型，利用后验概率等准则选择更优模型，并用实例展示其相对于其他方法的优势。
Abstract In this article we put forward a Bayesian approach for finding classification and regression tree (CART) models. The two basic components of this approach consist of prior specification and stochastic search. The basic idea is to have the prior induce a posterior distribution that will guide the stochastic search toward more promising CART models. As the search proceeds, such models can then be selected with a variety of criteria, such as posterior probability, marginal likelihood, residual sum of squares or misclassification rates. Examples are used to illustrate the potential superiority of this approach over alternative methods.