Past Event: Oden Institute Seminar

Computationally efficient Markov chain Monte Carlo methods for hierarchical Bayesian inverse problems

Sarah Vallelian, SAMSI and North Carolina State University

Abstract

In Bayesian inverse problems, the posterior distribution can be used to quantify uncertainty about the reconstructed solution. In practice, approximating the posterior requires Markov chain Monte Carlo (MCMC) algorithms, but these can be computationally expensive. We present a computationally efficient MCMC sampling scheme for ill-posed Bayesian inverse problems.