**Room**: ACS 362B

**Speaker**: Ruanui Nicholson (Lecturer, The University of Auckland)

**Title Talk**: On the use of (Linear) Surrogates in Bayesian Inverse Problems

**Abstract**: In this talk we consider the use of surrogate (forward) models to efficiently solve Bayesian inverse problems. The problems considered are from a range of applications but are all high dimensional problems resulting from the discretization of partial differential equations. We adopt the Bayesian approximation error approach to account for the model discrepancy, which is treated as an additional stochastic error term. We also prove a somewhat surprising result: under the assumption of a Gaussian prior and additive noise model the approximate posterior found by using a linear(-ised) surrogate is invariant to the choice of linear surrogate, so long as the model discrepancy is taken into account via the Bayesian approximation error approach. This is ongoing joint work with Noemi Petra, Umberto Villa, and Jari P. Kaipio.