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.
University of Glasgow UK.
Antony M
AM
Overstall
University of Southampton UK.
David C
DC
Woods
Bayesian emulation; Computer experiment; Gaussian process; Lightweight emulator; Non‐parametric regression