Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations

We consider the problem of designing for complex high-dimensional computer models that can be evaluated at different levels of accuracy. Ordinarily, this requires performing many expensive evaluations of the most accurate version of the computer model to obtain a reasonable coverage of the design sp...

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Veröffentlicht in:Technometrics Jg. 51; H. 4; S. 377 - 388
Hauptverfasser: Cumming, Jonathan A., Goldstein, Michael
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Alexandria Taylor & Francis 01.11.2009
The American Society for Quality and The American Statistical Association
American Society for Quality
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ISSN:0040-1706, 1537-2723
Online-Zugang:Volltext
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Zusammenfassung:We consider the problem of designing for complex high-dimensional computer models that can be evaluated at different levels of accuracy. Ordinarily, this requires performing many expensive evaluations of the most accurate version of the computer model to obtain a reasonable coverage of the design space. In some cases, it is possible to supplement the information from the accurate model evaluations with a large number of evaluations of a cheap, approximate version of the computer model to enable a more informed design choice. We describe an approach that combines the information from both the approximate model and the accurate model into a single multiscale emulator for the computer model. We then propose a design strategy for selecting a small number of expensive evaluations of the accurate computer model based on our multiscale emulator and a decomposition of the input parameter space. We illustrate our methodology with an example concerning a computer simulation of a hydrocarbon reservoir.
Bibliographie:SourceType-Scholarly Journals-1
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ISSN:0040-1706
1537-2723
DOI:10.1198/TECH.2009.08015