Date and Time: Wednesday, 13 February, 11.00-12.00
Location: EPSRC Centres for Doctoral Training Suite, South Kensington Campus, the ICSM Building, Level 4N, Room 402
Title Surrogate models in Bayesian inverse problems
Abstract: 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 probability 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. 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.