Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems

Goldstein, M.; Rougier, J.C.

doi:10.1137/s106482750342670x

Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems

Goldstein, M.; Rougier, J.C.

Authors

M. Goldstein

J.C. Rougier

Abstract

We outline a probabilistic framework for linking mathematical models to the physical systems that they represent, taking account of all sources of uncertainty including model and simulator imperfections. This framework is a necessary precondition for making probabilistic statements about the system on the basis of evaluations of computer simulators. We distinguish simulators according to their quality and the nature of their inputs. Where necessary, we introduce further hypothetical simulators as modelling constructs to account for imperfections in the available simulators and to unify the available simulators with the underlying system.

Citation

Goldstein, M., & Rougier, J. (2004). Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems. SIAM Journal on Scientific Computing, 26(2), 467-487. https://doi.org/10.1137/s106482750342670x

Journal Article Type	Article
Publication Date	Jan 1, 2004
Deposit Date	Aug 29, 2008
Publicly Available Date	Aug 29, 2008
Journal	SIAM Journal on Scientific Computing
Print ISSN	1064-8275
Electronic ISSN	1095-7197
Publisher	Society for Industrial and Applied Mathematics
Peer Reviewed	Peer Reviewed
Volume	26
Issue	2
Pages	467-487
DOI	https://doi.org/10.1137/s106482750342670x
Keywords	Direct simulator, Uncertainty analysis, Indirect simulator, Top simulator, Measurable inputs, Tuning inputs, Bayesian inference, Calibration, History matching, Calibrated prediction.