TY - JOUR
T1 - Bayesian treed response surface models
AU - Chipman, Hugh
AU - George, Edward I.
AU - Gramacy, Robert B.
AU - Mcculloch, Robert
PY - 2013/7/1
Y1 - 2013/7/1
N2 - Tree-based regression and classification, popularized in the 1980s with the advent of the classification and regression trees (CART) has seen a recent resurgence in popularity alongside a boom in modern computing power. The new methodologies take advantage of simulation-based inference, and ensemble methods, to produce higher fidelity response surfaces with competitive out-of-sample predictive performance while retaining many of the attractive features of classic trees: thrifty divide-and-conquer nonparametric inference, variable selection and sensitivity analysis, and nonstationary modeling features. In this paper, we review recent advances in Bayesian modeling for trees, from simple Bayesian CART models, treed Gaussian process, sequential inference via dynamic trees, to ensemble modeling via Bayesian additive regression trees (BART). We outline open source R packages supporting these methods and illustrate their use.
AB - Tree-based regression and classification, popularized in the 1980s with the advent of the classification and regression trees (CART) has seen a recent resurgence in popularity alongside a boom in modern computing power. The new methodologies take advantage of simulation-based inference, and ensemble methods, to produce higher fidelity response surfaces with competitive out-of-sample predictive performance while retaining many of the attractive features of classic trees: thrifty divide-and-conquer nonparametric inference, variable selection and sensitivity analysis, and nonstationary modeling features. In this paper, we review recent advances in Bayesian modeling for trees, from simple Bayesian CART models, treed Gaussian process, sequential inference via dynamic trees, to ensemble modeling via Bayesian additive regression trees (BART). We outline open source R packages supporting these methods and illustrate their use.
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U2 - 10.1002/widm.1094
DO - 10.1002/widm.1094
M3 - Article
AN - SCOPUS:84880139636
SN - 1942-4787
VL - 3
SP - 298
EP - 305
JO - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
JF - Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
IS - 4
ER -