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Small Sample Bayesian Designs for Complex High-Dimensional Models

Summary: [This abstract is based on the authors' abstract.] Designing for complex high-dimensional computer models ordinarily requires performing expensive evaluations of the most accurate version of the computer model. It is sometimes possible to supplement this information with evaluations of cheaper approximate versions of the computer to obtain a more informed design choice. An approach is described that uses this combined information to create a single multiscale emulator for the computer model. A design strategy is then proposed for selecting a few of the expensive factors of the accurate model based on the emulator and a decomposition of the input parameter space. An example involving a computer simulation of a hydrocarbon reservoir illustrates the method.

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  • Topics: Design of Experiments
  • Keywords: Computer models, Bayesian methods, Design of experiments (DOE), Approximation, Simulations
  • Author: Cumming, Jonathan A.; Goldstein, Michael
  • Journal: Technometrics