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Bayesian calibration, history matching and model discrepancy

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UNQW04 - UQ for inverse problems in complex systems

Bayesian calibration and history matching are both well established tools for solving inverse problems: finding model inputs to make model outputs match observed data as closely as possible. I will discuss and compare both, within the context of decision-making. I will discuss the sometimes contentious issue of model discrepancy: how and whether we might account for an imperfect or misspecified model within the inference procedure. I will also present some work on history matching of a high dimensional individual based HIV transmission model (joint work with I. Andrianakis, N. McCreesh, I. Vernon, T. J. McKinley, R. N. Nsubuga, M. Goldstein and R. J. White).

This talk is part of the Isaac Newton Institute Seminar Series series.

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