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Activity Number: 533
Type: Contributed
Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 AM
Sponsor: Section on Physical and Engineering Sciences
Abstract - #309314
Title: Learning About Physical Parameters: The Importance of Model Discrepancy
Author(s): Jenny Brynjarsdottir*+ and Anthony O'Hagan
Companies: Duke University and The University of Sheffield

Computer models are widely used to predict the behaviour of complex physical systems. Observations of the physical system are used to learn about the values of parameters within the model (calibration). The parameters in the model may be of intrinsic scientific interest, so that learning about them contributes to the underlying science. An important source of uncertainty in this kind of analysis is model discrepancy, the difference between reality and the computer model output. We illustrate through a simple example that an analysis that does not account for model discrepancy will lead to biased and over-confident parameter estimates and predictions. The challenge with incorporating model discrepancy in a statistical analysis of computer models is the confounding with calibration parameters, which will only be resolved with meaningful priors. For our simple example, we model the model-discrepancy via a Gaussian Process and demonstrate that by accounting for model discrepancy our prediction within the range of data is correct. However, only with realistic priors on model discrepancy do we uncover true parameters.

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