Bayesian treed response surface models
Bayesian treed response surface models
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贝叶斯树响应面模型
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
R. McCulloch
中科院分区：
文献类型：
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作者：
H. Chipman;E. George;R. Gramacy;R. McCulloch
基于树的回归和分类，在20世纪80年代随着分类和回归树（CART）的出现而流行起来，随着现代计算能力的蓬勃发展，最近又重新流行起来。新方法利用基于模拟的推理和集成方法，产生具有竞争性样本外预测性能的更高保真度的响应面，同时保留了经典树的许多吸引人的特征：节俭的分而治之的非参数推理，变量选择和敏感性分析，以及非平稳建模特征。本文综述了树的贝叶斯建模的最新进展，从简单的贝叶斯CART模型，树状高斯过程，通过动态树的顺序推理，到通过贝叶斯加性回归树（BART）的集成建模。我们概述了支持这些方法的开源R包，并说明了它们的用法。
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.