University of Birmingham > Talks@bham > Applied Mathematics Seminar Series > Surrogate Models in Large-Scale Bayesian Inverse Problems

Surrogate Models in Large-Scale Bayesian Inverse Problems

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  • UserAretha Teckentrup, University of Edinburgh
  • ClockThursday 30 November 2017, 12:00-13:00
  • HouseNuffield G17.

If you have a question about this talk, please contact Meurig Gallagher.

We are interested in the inverse problem of estimating unknown parameters in a mathematical model from observed data. We follow the Bayesian approach, in which the solution to the inverse problem is the distribution of the unknown parameters conditioned on the observed data, the so-called posterior distribution. We are particularly interested in the case where the mathematical model is non-linear and expensive to simulate, for example given by a partial differential equation. In this case, the solution of the inverse problem quickly becomes computationally infeasible in practice.

To alleviate this problem, we consider the use of surrogate models to approximate the Bayesian posterior distribution. We present a general framework for the analysis of the error introduced in the posterior distribution, and discuss particular examples of surrogate models such as Gaussian process emulators and randomised misfit approaches.

This talk is part of the Applied Mathematics Seminar Series series.

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