Bayesian CART Model Search
Journal of the American Statistical AssociationPublished 1 September 1998
Hugh Chipman, Edward I. George, Robert E. McCulloch
Citations713
SJR quartileQ1
SJR score4.10
SNIP3.08
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TL;DR
A Bayesian approach for finding classification and regression tree (CART) models by having the prior induce a posterior distribution that will guide the stochastic search toward more promising CART models.
Abstract
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.
Keywords
Computer ScienceMathematics
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