Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model
Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model
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.
483-505
Overstall, Antony
c1d6c8bd-1c5f-49ee-a845-ec9ec7b20910
Woods, Dave
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
August 2016
Overstall, Antony
c1d6c8bd-1c5f-49ee-a845-ec9ec7b20910
Woods, Dave
ae21f7e2-29d9-4f55-98a2-639c5e44c79c
Overstall, Antony and Woods, Dave
(2016)
Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model.
Journal of the Royal Statistical Society. Series C: Applied Statistics, 65 (4), .
(doi:10.1111/rssc.12141).
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.
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e-pub ahead of print date: 1 March 2016
Published date: August 2016
Organisations:
Statistics
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Local EPrints ID: 401178
URI: http://eprints.soton.ac.uk/id/eprint/401178
ISSN: 0035-9254
PURE UUID: dc8c5ece-e20a-449e-87a5-99231d6aaf53
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Date deposited: 05 Oct 2016 14:23
Last modified: 15 Mar 2024 03:27
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