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Hybrid Samplers for Ill‐Posed Inverse Problems
Author(s) -
HERBEI RADU,
McKEAGUE IAN W.
Publication year - 2009
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/j.1467-9469.2009.00649.x
Subject(s) - mathematics , markov chain monte carlo , posterior probability , inverse problem , metropolis–hastings algorithm , bayesian probability , prior probability , inverse , ergodicity , random walk , markov chain , mathematical optimization , algorithm , statistics , mathematical analysis , geometry
. 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.