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Assessing simulator uncertainty using evaluations from several different simulators

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If you have a question about this talk, please contact Mustapha Amrani.

Design and Analysis of Experiments

Any simulator-based prediction must take account of the discrepancy between the simulator and the underlying system. In physical systems, such as climate, this discrepancy has a complex, unknown structure that makes direct elicitation very demanding. Here, we propose a fundamentally different framework to that currently in use and consider information in a collection of simulator-evaluations, known as a Multi-Model Ensemble (MME). We justify our approach both in terms of its transparency, tractability, and consistency with standard practice in, say, Climate Science. The statistical modelling framework is that of second-order exchangeability, within a Bayes linear treatment. We apply our methods based on a reconstruction of boreal winter surface temperature.

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

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