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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 te...

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Detalles Bibliográficos
Autores principales: Overstall, Antony M., Woods, David C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4991306/
https://www.ncbi.nlm.nih.gov/pubmed/27609994
http://dx.doi.org/10.1111/rssc.12141
Descripción
Sumario: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.