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Small sample Bayesian designs for complex high-dimensional models based on information gained using fast approximations.

Cumming, J. A. and Goldstein, M. (2009) 'Small sample Bayesian designs for complex high-dimensional models based on information gained using fast approximations.', Technometrics., 51 (4). pp. 377-388.


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.

Item Type:Article
Full text:(AM) Accepted Manuscript
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Publisher statement:This is an Accepted Manuscript of an article published by Taylor & Francis Group in Technometrics on 01/01/2012, available online at:
Date accepted:No date available
Date deposited:08 August 2016
Date of first online publication:November 2009
Date first made open access:No date available

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