This recording is in the process of being subtitled. We aim to have edited captions available within 2 weeks of publishing.
18 October 2021
Aretha Teckentrup (Edinburgh) - Surrogate models in Bayesian inverse problems
Model calibration involves
estimating unknown parameters in a mathematical model from observed data. We
follow the Bayesian approach, in which the solution to this 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. In this
talk, we consider the use of surrogate models (also known as emulators or
reduced order 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.