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Bayesian integrative analysis for multi-fidelity computer experiments
Wei, Yunfei1,2; Xiong, Shifeng2
AbstractThis paper proposes a Bayesian integrative analysis method for linking multi-fidelity computer experiments. Instead of assuming covariance structures of multivariate Gaussian process models, we handle the outputs from different levels of accuracy as independent processes and link them via a penalization method that controls the distance between their overall trends. Based on the priors induced by the penalty, we build Bayesian prediction models for the output at the highest accuracy. Simulated and real examples show that the proposed method is better than existing methods in terms of prediction accuracy for many cases.
KeywordCorrelated priors Gaussian process Kriging penalization uncertainty quantification
Funding ProjectChinese Ministry of Science and Technology of the People's Republic of China[2016YFF0203801] ; National Natural Science Foundation of China[11671386] ; National Natural Science Foundation of China[11871033] ; Key Laboratory of Systems and Control, CAS
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000472110500004
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Document Type期刊论文
Corresponding AuthorXiong, Shifeng
Affiliation1.Univ Chinese Acad Sci, Sch Math Sci, Beijing, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Wei, Yunfei,Xiong, Shifeng. Bayesian integrative analysis for multi-fidelity computer experiments[J]. JOURNAL OF APPLIED STATISTICS,2019,46(11):1973-1987.
APA Wei, Yunfei,&Xiong, Shifeng.(2019).Bayesian integrative analysis for multi-fidelity computer experiments.JOURNAL OF APPLIED STATISTICS,46(11),1973-1987.
MLA Wei, Yunfei,et al."Bayesian integrative analysis for multi-fidelity computer experiments".JOURNAL OF APPLIED STATISTICS 46.11(2019):1973-1987.
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