Using emulators to estimate uncertainty in complex models. The managing uncertainty in complex model projects has been developing methods for estimating uncertainty in complex models using emulators. Emulators are statistical descriptions of our beliefs about the models (or simulators). They can also be thought of as interpolators of simulator outputs between previous runs. Because they are quick to run, emulators can be used to carry out calculations that would otherwise require large numbers of simulator runs, for example Monte Carlo uncertainty calculations. par Both Gaussian and Bayes linear emulators are explained and examples are given. One of the outputs of the MUCM project is the MUCM toolkit, an on-line recipe book for emulator based methods. Using the toolkit as our basis we illustrate the breadth of applications that can be addressed by emulator methodology and detail some of the methodology. We cover sensitivity and uncertainty analysis and describe in less detail other aspects such as how emulators can also be used to calibrate complex computer simulators and how they can be modified for use with stochastic simulators.
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References in zbMATH (referenced in 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Glaws, Andrew; Constantine, Paul G.; Cook, R. Dennis: Inverse regression for ridge recovery: a data-driven approach for parameter reduction in computer experiments (2020)
- Lan, Shiwei; Bui-Thanh, Tan; Christie, Mike; Girolami, Mark: Emulation of higher-order tensors in manifold Monte Carlo methods for Bayesian inverse problems (2016)
- Challenor, Peter: Using emulators to estimate uncertainty in complex models (2012)