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June 2009 A dynamic modelling strategy for Bayesian computer model emulation
Fei Liu, Mike West
Bayesian Anal. 4(2): 393-411 (June 2009). DOI: 10.1214/09-BA415

Abstract

Computer model evaluation studies build statistical models of deterministic simulation-based predictions of field data to then assess and criticize the computer model and suggest refinements. Computer models are often expensive computationally: statistical models that adequately emulate their key features can be very much more efficient. Gaussian process models are often used as emulators, but the resulting computations lack the ability to scale to higher-dimensional, time-dependent or functional outputs. For some such problems, especially for contexts of time series outputs, building emulators using dynamic linear models provides a computationally attractive alternative as well as a flexible modelling approach capable of emulating a broad range of stochastic structures underlying the input-output simulations. We describe this here, combining Bayesian multivariate dynamic linear models with Gaussian process modelling in an effective manner, and illustrate the approach with data from a hydrological simulation model. The general strategy will be useful for other computer model evaluation studies with time series or functional outputs.

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Fei Liu. Mike West. "A dynamic modelling strategy for Bayesian computer model emulation." Bayesian Anal. 4 (2) 393 - 411, June 2009. https://doi.org/10.1214/09-BA415

Information

Published: June 2009
First available in Project Euclid: 22 June 2012

zbMATH: 1330.65034
MathSciNet: MR2507369
Digital Object Identifier: 10.1214/09-BA415

Rights: Copyright © 2009 International Society for Bayesian Analysis

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Vol.4 • No. 2 • June 2009
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