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UQ perspectives on approximate Bayesian computation (ABC)

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UNQW01 - Key UQ methodologies and motivating applications

Approximate Bayesian computation (ABC) methods are widely used in some scientific disciplines for fitting stochastic simulators to data. They are primarily used in situations where the likelihood function of the simulator is unknown, but where it is possible to easily sample from the simulator. Methodological development of ABC methods has primarily focused on computational efficiency and tractability, rather than on careful uncertainty modelling. In this talk I'll briefly introduce ABC and its various extensions, and then interpret it from a UQ perspective and suggest how it may best be modified.

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

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