Bayesian CART Model Search
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Statistics Papers
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binary trees
Markov chain Monte Carlo
mixture models
model selection
model uncertainty
stochastic search
Statistics and Probability
Markov chain Monte Carlo
mixture models
model selection
model uncertainty
stochastic search
Statistics and Probability
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Chipman, Hugh A
George, Edward I
McCulloch, Robert E
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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.
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1998
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Journal of the American Statistical Association