A Metropolis-Hastings-within-Gibbs sampler for nonlinear hierarchical-Bayesian inverse problems

DOI10.1007/978-3-030-04161-8_1zbMath1419.65002OpenAlexW2922302762MaRDI QIDQ2001214

Tiangang Cui, Johnathan M. Bardsley

Publication date: 2 July 2019

Full work available at URL: https://doi.org/10.1007/978-3-030-04161-8_1

zbMATH Keywords

linear inverse problemrandomize-then-optimize (RTO) methodsampling posterior distribution

Mathematics Subject Classification ID

Related Items (2)

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Cites Work

• MCMC-Based Image Reconstruction with Uncertainty Quantification
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