Statistics Colloquium
November 6 (Fri) 11 am
Alavi Commons Room, 6625 Everett Tower
Hybrid samplers for ill-posed inverse problems
Radu Herbei
Department of Statistics
The Ohio State University
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.
In this talk we 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 when
estimating the coefficients of a partial differential
equation in a Bayesian framework.
All Statistics students are expected to attend.
