Title
Hybrid samplers for ill-posed inverse problems
Speaker
Radu Herbei, The Ohio State University
Abstract
In the Bayesian approach to ill-posed inverse problems, regularization is imposed by specifying a prior distribution on the parameters of interest and MCMC samplers are used to extract information about its posterior distribution. The aim of this paper is to investigate the convergence properties of the random-scan random walk Metropolis (RSM) algorithm for posterior distributions in ill-posed inverse problems. We provide an accessible set of sufficient conditions, in terms of the observational model and the prior, to ensure geometric ergodicity of RSM samplers of the posterior distribution. We illustrate how these conditions can be checked in an application to the inversion of oceanographic tracer data.
Meet the speaker in Room 212 Cockins Hall after the talk. Refreshments will be served.
