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2019 | OriginalPaper | Buchkapitel

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

verfasst von : Johnathan M. Bardsley, Tiangang Cui

Erschienen in: 2017 MATRIX Annals

Verlag: 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.

Metadaten
Titel
A Metropolis-Hastings-Within-Gibbs Sampler for Nonlinear Hierarchical-Bayesian Inverse Problems
verfasst von
Johnathan M. Bardsley
Tiangang Cui
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-04161-8_1

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