Hybrid Samplers for Ill-Posed Inverse Problems

Authors: HERBEI, RADU¹; McKEAGUE, IAN W.²

Source: Scandinavian Journal of Statistics, Volume 36, Number 4, December 2009 , pp. 839-853(15)

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Abstract:

In the Bayesian approach to ill-posed inverse problems, regularization is imposed by specifying a prior distribution on the parameters of interest and Markov chain Monte Carlo 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.

Keywords: advection-diffusion; Bayesian regularization; geometric ergodicity; Markov chain Monte Carlo; ocean circulation; random-scan Metropolis

Document Type: Research article

DOI: http://dx.doi.org/10.1111/j.1467-9469.2009.00649.x

Affiliations: 1: Department of Statistics, The Ohio State University 2: Department of Biostatistics, Columbia University

Publication date: 2009-12-01