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"Design for ABC and history matching with Gaussian processes"

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If you have a question about this talk, please contact Alison Quenault.

Abstract: History matching and approximate Bayesian computation (ABC) are techniques for parameter estimation for complex models. In both cases, Gaussian processes can be used to accelerate inference by building an emulator/surrogate model either of the simulator itself, or of a summary such as the likelihood function. Design approaches for building emulators usually focus on how to build accurate global approximations. However, if our aim is calibration, then this can be wasteful. In this talk I will discuss the use of Gaussian processes, and describe design strategies that can be used for history matching and calibration, both in a deterministic and stochastic setting. Entropy based designs that minimize the uncertainty in the classification surface are particularly useful, and I will show how these can be efficiently computed using Bayesian optimisation.

This talk is part of the MRC Biostatistics Unit Seminars series.

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