Bayesian treed models

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

Research output: Contribution to journalArticlepeer-review

122 Scopus citations


When simple parametric models such as linear regression fail to adequately approximate a relationship across an entire set of data, an alternative may be to consider a partition of the data, and then use a separate simple model within each subset of the partition. Such an alternative is provided by a treed model which uses a binary tree to identify such a partition. However, treed models go further than conventional trees (e.g. CART, C4.5) by fitting models rather than a simple mean or proportion within each subset. In this paper, we propose a Bayesian approach for finding and fitting parametric treed models, in particular focusing on Bayesian treed regression. The potential of this approach is illustrated by a cross-validation comparison of predictive performance with neural nets, MARS, and conventional trees on simulated and real data sets.

Original languageEnglish (US)
Pages (from-to)299-320
Number of pages22
JournalMachine Learning
Issue number1-3
StatePublished - Jul 2002
Externally publishedYes


  • Binary trees
  • Markov chain Monte Carlo
  • Model selection
  • Stochastic search

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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