A Metropolis-Hastings-within-Gibbs sampler for nonlinear hierarchical-Bayesian inverse problems
type: Item
instance of: scholarly article
title: A Metropolis-Hastings-within-Gibbs sampler for nonlinear hierarchical-Bayesian inverse problems
zbMATH Open document ID: 1419.65002
DOI: 10.1007/978-3-030-04161-8_1
author: Johnathan M. Bardsley
author: Tiangang Cui
publication date: 2019-07-02T00:00:00Z
Mathematics Subject Classification ID: 65C50
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 65C05
zbMATH DE Number: 7075708
zbMATH Keywords: randomize-then-optimize (RTO) method
zbMATH Keywords: sampling posterior distribution
zbMATH Keywords: linear inverse problem
MaRDI profile type: Publication
full work available at URL: 978-3-030-04161-8 1
OpenAlex ID: W2922302762
cites work: Q4556297
cites work: MCMC-based image reconstruction with uncertainty quantification
cites work: Randomize-Then-Optimize: A Method for Sampling from Posterior Distributions in Nonlinear Inverse Problems
cites work: Q4828566
description: scientific article; zbMATH DE number 7075708