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2019 | OriginalPaper | Chapter

A Metropolis-Hastings-Within-Gibbs Sampler for Nonlinear Hierarchical-Bayesian Inverse Problems

Authors : Johnathan M. Bardsley, Tiangang Cui

Published in: 2017 MATRIX Annals

Publisher: Springer International Publishing

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We investigate the use of the randomize-then-optimize (RTO) method as a proposal distribution for sampling posterior distributions arising in nonlinear, hierarchical Bayesian inverse problems. Specifically, we extend the hierarchical Gibbs sampler for linear inverse problems to nonlinear inverse problems by embedding RTO-MH within the hierarchical Gibbs sampler. We test the method on a nonlinear inverse problem arising in differential equations.

Metadata
Title
A Metropolis-Hastings-Within-Gibbs Sampler for Nonlinear Hierarchical-Bayesian Inverse Problems
Authors
Johnathan M. Bardsley
Tiangang Cui
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-04161-8_1

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