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 |
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| Hauptverfasser: | , |
| 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 |
| Schlagworte: | |
| 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. |
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| Bibliographie: | SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 |
| ISSN: | 0040-1706 1537-2723 |
| DOI: | 10.1198/TECH.2009.08015 |