Metropolis-Hastings Algorithms in Function Space for Bayesian Inverse Problems

Handle URI:
http://hdl.handle.net/10754/624113
Title:
Metropolis-Hastings Algorithms in Function Space for Bayesian Inverse Problems
Authors:
Ernst, Oliver
Abstract:
We consider Markov Chain Monte Carlo methods adapted to a Hilbert space setting. Such algorithms occur in Bayesian inverse problems where the solution is a probability measure on a function space according to which one would like to integrate or sample. We focus on Metropolis-Hastings algorithms and, in particular, we introduce and analyze a generalization of the existing pCN-proposal. This new proposal allows to exploit the geometry or anisotropy of the target measure which in turn might improve the statistical efficiency of the corresponding MCMC method. Numerical experiments for a real-world problem confirm the improvement.
Conference/Event name:
Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)
Issue Date:
7-Jan-2015
Type:
Presentation
Additional Links:
http://mediasite.kaust.edu.sa/Mediasite/Play/4ec77ede05834d88930308bc5c5d033f1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52
Appears in Collections:
Conference on Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)

Full metadata record

DC FieldValue Language
dc.contributor.authorErnst, Oliveren
dc.date.accessioned2017-06-05T08:35:49Z-
dc.date.available2017-06-05T08:35:49Z-
dc.date.issued2015-01-07-
dc.identifier.urihttp://hdl.handle.net/10754/624113-
dc.description.abstractWe consider Markov Chain Monte Carlo methods adapted to a Hilbert space setting. Such algorithms occur in Bayesian inverse problems where the solution is a probability measure on a function space according to which one would like to integrate or sample. We focus on Metropolis-Hastings algorithms and, in particular, we introduce and analyze a generalization of the existing pCN-proposal. This new proposal allows to exploit the geometry or anisotropy of the target measure which in turn might improve the statistical efficiency of the corresponding MCMC method. Numerical experiments for a real-world problem confirm the improvement.en
dc.relation.urlhttp://mediasite.kaust.edu.sa/Mediasite/Play/4ec77ede05834d88930308bc5c5d033f1d?catalog=ca65101c-a4eb-4057-9444-45f799bd9c52en
dc.titleMetropolis-Hastings Algorithms in Function Space for Bayesian Inverse Problemsen
dc.typePresentationen
dc.conference.dateJanuary 6-9, 2015en
dc.conference.nameAdvances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2015)en
dc.conference.locationKAUSTen
dc.contributor.institutionTechnische Universit├Ąt Chemnitzen
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