Sequential design for computer experiments with a flexible Bayesian additive model

DOI10.1002/cjs.11156zbMath1349.62366arXiv1203.1078OpenAlexW2114452512MaRDI QIDQ2856566

Pritam Ranjan, Hugh A. Chipman, Wei-wei Wang

Publication date: 29 October 2013

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1203.1078

zbMATH Keywords

global optimizationexpected improvementadditive regression treesnonstationary simulatorstidal power model

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to environmental and related topics (62P12) Sequential statistical design (62L05)

Related Items

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Uses Software

• EGO
• BayesTree
• lhs
• BartPy

Cites Work

• Unnamed Item
• BART: Bayesian additive regression trees
• Cases for the nugget in modeling computer experiments
• Efficient global optimization of expensive black-box functions
• The design and analysis of computer experiments.
• Bayesian curve-fitting with free-knot splines
• Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
• An adaptive sampling scheme guided by BART—with an application to predict processor performance