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Parameter inference, model error and the goals of calibration

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UNQW04 - UQ for inverse problems in complex systems

I have some data, a mathematical model describing a process in the real world that produced that data and I would like to learn something about the real world. We would typically formulate this as an inverse problem and apply our favourite techniques for solving it (e.g. Bayesian calibration or history matching), ultimately providing inference for those parameters in our mathematical model that are consistent with the data. Does this make sense? In this talk, I will use climate science as a lens through which we can look at how mathematical models are viewed and treated by the scientific community, and consider UQ approaches to inverse problems and how they might fit and ask whether it matters if they don't.

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

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