Bayesian CART model search

Hugh A. Chipman, Edward I. George, Robert E. McCulloch

Research output: Contribution to journalArticlepeer-review

411 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)935-948
Number of pages14
JournalJournal of the American Statistical Association
Volume93
Issue number443
DOIs
StatePublished - Sep 1 1998

Keywords

  • Binary trees
  • Markov chain Monte Carlo
  • Mixture models
  • Model selection
  • Model uncertainty
  • Stochastic search

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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