Overstall, Antony M. and Woods, David C.
Bayesian lightweight emulators for multivariate computer models , Southampton, GB Southampton Statistical Sciences Research Institute 27pp.
(S3RI Methodology Working Papers, M11/01).
Statistical emulators for the outputs of complex computer codes (simulators) are typically constructed using nonparametric regression methods, such as Gaussian Process (GP) regression. For many simulators, emulators based on parametric models may provide adequate descriptions whilst enabling straightforward and computationally inexpensive fitting, inference and prediction. We place such so called “lightweight” emulators into the same Bayesian framework as the more usual nonparametric emulators, and provide methodology for their application to two novel examples with multivariate output: an emergency-relief simulator and a low-level atmospheric dispersion simulator. For the former, the inputs to the simulator are both continuous and categorical, and a comparison is made to GP emulators; for the latter, the output is zeroinflated and an appropriate emulator is developed from a Tobit model. In each case, sensitivity analyses are performed to identify the inputs to the simulator that have a substantive impact on the response, using both traditional methods and Bayesian model selection.
||bayesian linear regression, gaussian process, markov chain monte carlo model composition, tobit model, zero-inflated response
|14 January 2011||Published|
||14 Jan 2011 15:31
||18 Apr 2017 03:27
|Further Information:||Google Scholar|
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