Reified Bayesian modelling and inference for physical systems

By Michael L. Goldstein, Jonathan Rougier. 2009

171 citations recorded by the source corpus.

We describe an approach, termed reified analysis, for linking the behaviour of mathematical models with inferences about the physical systems which the models represent. We describe the logical basis for the approach, based on coherent assessment of the implications of deficiencies in the mathematical model. We show how the statistical analysis may be carried out by specifying stochastic relationships between the model that we have, improved versions of the model that we might construct, and the system itself. We illustrate our approach with an example concerning the potential shut-down of the Thermohaline Circulation in the Atlantic Ocean.

Subfields: Artificial Intelligence, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/4197bfe81651b92de7df4f48d3ff94ca3bb2f4b4

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Citations in the corpus

Efficient Emulators for Multivariate Deterministic Functions (2008, methodology): “This has been discussed in Kennedy and O’Hagan (2001), Bayarri et al.”
Learning about physical parameters: the importance of model discrepancy (2014, methodology): “Since then, their inferential ideas and modeling framework have been widely adopted and further developed, e.g. Higdon et al (2004, 2008), Qian and Wu (2008), Goldstein and Rougier (2009), Bayarri et al (2007), Gramacy and Lee (2008).”
Modelling everything everywhere: a new approach to decision-making for water management under uncertainty (2012, methodology): “This is not just a difficulty in applying formal statistical methods as suggested by O’Hagan & Oakley (2004) or more recently in the reification approach of Goldstein & Rougier (2009); the list of epistemic errors is long and this is a generic problem.”
Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors (2010, result): “This makes them subtly different from the notions of “model inadequacy” and “discrepancy” introduced elsewhere when discussing model structural errors [Goldstein and Rougier, 2009; Kennedy and O’Hagan, 2001].”