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Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model.


ABSTRACT: We present a common framework for Bayesian emulation methodologies for multivariate output simulators, or computer models, that employ either parametric linear models or non-parametric Gaussian processes. Novel diagnostics suitable for multivariate covariance separable emulators are developed and techniques to improve the adequacy of an emulator are discussed and implemented. A variety of emulators are compared for a humanitarian relief simulator, modelling aid missions to Sicily after a volcanic eruption and earthquake, and a sensitivity analysis is conducted to determine the sensitivity of the simulator output to changes in the input variables. The results from parametric and non-parametric emulators are compared in terms of prediction accuracy, uncertainty quantification and scientific interpretability.

SUBMITTER: Overstall AM 

PROVIDER: S-EPMC4991306 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model.

Overstall Antony M AM   Woods David C DC  

Journal of the Royal Statistical Society. Series C, Applied statistics 20160301 4


We present a common framework for Bayesian emulation methodologies for multivariate output simulators, or computer models, that employ either parametric linear models or non-parametric Gaussian processes. Novel diagnostics suitable for multivariate covariance separable emulators are developed and techniques to improve the adequacy of an emulator are discussed and implemented. A variety of emulators are compared for a humanitarian relief simulator, modelling aid missions to Sicily after a volcani  ...[more]

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