Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems

Goldstein, M.; Rougier, J.C.

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.
• Public URL: https://durham-repository.worktribe.com/output/1597252