Bayesian treed Gaussian process models with an application to computer modeling

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dc.creatorGramacy, Robert B.
dc.creatorLee, Herbert K. H.
dc.date2007-10-24
dc.date2009-03-17
dc.date.accessioned2026-07-07T12:52:31Z
dc.date.available2026-07-07T12:52:31Z
dc.descriptionMotivated by a computer experiment for the design of a rocket booster, this paper explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. The methodological developments and statistical computing details which make this approach efficient are described in detail. In addition to providing an analysis of the rocket booster simulator, our approach is demonstrated to be effective in other arenas.
dc.description32 pages, 9 figures, to appear in the Journal of the American Statistical Association
dc.identifierhttps://arxiv.org/abs/0710.4536
dc.identifierhttp://arxiv.org/abs/0710.4536
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/223318
dc.subjectMethodology
dc.subjectApplications
dc.subjectComputation
dc.titleBayesian treed Gaussian process models with an application to computer modeling
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