.png){kind=icon}
Return
Bayesian treed response surface models
.png){kind=icon}
PRE
AI
DOI：10.1002/widm.1094.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2013 Wiley Periodicals, Inc.
Keywords:
OPTIMIZATION
SEARCH
DESIGN
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.7
Papers:
548
Citations:
5.3K
Organization
Cited Papers
.png){kind=icon}
ShareSave
ShareSaveShareSaveShareSaveShareSaveShareSave
View more