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Probabilistic formulations for transferring inferences from mathematical models to physical systems.

Goldstein, M. and Rougier, J. C. (2004) 'Probabilistic formulations for transferring inferences from mathematical models to physical systems.', SIAM journal on scientific computing., 26 (2). pp. 467-487.

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.

Item Type:Article
Keywords:Direct simulator, Uncertainty analysis, Indirect simulator, Top simulator, Measurable inputs, Tuning inputs, Bayesian inference, Calibration, History matching, Calibrated prediction.
Full text:PDF - Published Version (196Kb)
Status:Peer-reviewed
Publisher Web site:http://dx.doi.org/10.1137/S106482750342670X
Publisher statement:© 2004 Society for Industrial and Applied Mathematics
Record Created:29 Aug 2008
Last Modified:24 Aug 2011 16:41

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