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dc.contributor.authorJohnson, Rachel T.
dc.contributor.authorMontgomery, Douglas C.
dc.contributor.authorJones, Bradley
dc.date.accessioned2016-01-20T22:51:16Z
dc.date.available2016-01-20T22:51:16Z
dc.date.issued2011
dc.identifier.citationInternational Experimental Design and Process Optimisation, v. 2, no. 1, 2011, pp. 1-18.en_US
dc.identifier.urihttp://hdl.handle.net/10945/47617
dc.descriptionApproved for public release; distribution is unlimiteden_US
dc.description.abstractThe Gaussian process (GASP) model has found widespread use as a surrogate model for results from deterministic computer model output. In this paper, we compare the fits of GASP models to specific space-filling designs based on their accuracy in predicting responses at previously unsampled locations. This is done empirically using several test functions. We demonstrate that no one space-filling design outperforms another with respect to prediction accuracy. We also found that while the GASP model is substantially easier to fit using the cubic correlation function than with the Gaussian correlation function, its prediction accuracy is not quite as good as the Gaussian correlation function for the chosen test functions especially for larger sample sizes. The best way to improve prediction accuracy is to increase the number of simulation runs, which suggests that the efficient augmentation of space filling designs is an important area for further research.en_US
dc.format.extent18 p.en_US
dc.publisherInderscience Enterprises, Ltd.en_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleAn empirical study of the prediction performance of space-filling designsen_US
dc.typeArticleen_US
dc.contributor.corporateNaval Postgraduate School (U.S.)en_US
dc.contributor.departmentOperations Researchen_US
dc.subject.authorCorrelation functionsen_US
dc.subject.authorCFsen_US
dc.subject.authorGaussian process modelsen_US
dc.subject.authorJackknife plotsen_US
dc.subject.authorSample sizeen_US
dc.subject.authorComputer experimentsen_US


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