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dc.contributor.authorLatz, Jonas
dc.contributor.authorMadrigal-Cianci, Juan P.
dc.contributor.authorNobile, Fabio
dc.contributor.authorTempone, Raul
dc.date.accessioned2021-08-31T06:07:26Z
dc.date.available2020-03-24T12:53:42Z
dc.date.available2021-08-31T06:07:26Z
dc.date.issued2021-08-30
dc.date.submitted2020-10-05
dc.identifier.citationLatz, J., Madrigal-Cianci, J. P., Nobile, F., & Tempone, R. (2021). Generalized parallel tempering on Bayesian inverse problems. Statistics and Computing, 31(5). doi:10.1007/s11222-021-10042-6
dc.identifier.issn0960-3174
dc.identifier.issn1573-1375
dc.identifier.doi10.1007/s11222-021-10042-6
dc.identifier.urihttp://hdl.handle.net/10754/662284
dc.description.abstractAbstractIn the current work we present two generalizations of the Parallel Tempering algorithm in the context of discrete-time Markov chain Monte Carlo methods for Bayesian inverse problems. These generalizations use state-dependent swapping rates, inspired by the so-called continuous time Infinite Swapping algorithm presented in Plattner et al. (J Chem Phys 135(13):134111, 2011). We analyze the reversibility and ergodicity properties of our generalized PT algorithms. Numerical results on sampling from different target distributions, show that the proposed methods significantly improve sampling efficiency over more traditional sampling algorithms such as Random Walk Metropolis, preconditioned Crank–Nicolson, and (standard) Parallel Tempering.
dc.description.sponsorshipWe would like to thank the anonymous reviewers for helpful suggestions that significantly improved this work.This publication was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under award numbers URF/1/2281-01-01 and URF/1/2584-01-01 in the KAUST Competitive Research Grants Programs- Round 3 and 4, respectively, and the Alexander von Humboldt Foundation. Jonas Latz acknowledges support by the Deutsche Forschungsgemeinschaft (DFG) through the TUM International Graduate School of Science and Engineering (IGSSE) within the project 10.02 BAYES. Juan P. Madrigal-Cianci and Fabio Nobile also acknowledge support from the Swiss Data Science Center (SDSC) Grant p18-09.
dc.description.sponsorshipOpen Access funding provided by EPFL Lausanne.
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttps://link.springer.com/10.1007/s11222-021-10042-6
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleGeneralized parallel tempering on Bayesian inverse problems
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.identifier.journalStatistics and Computing
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
dc.contributor.institutionSB-MATH-CSQI, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
dc.contributor.institutionAlexander von Humboldt professor in Mathematics of Uncertainty Quantification, RWTH Aachen University, Aachen, Germany.
dc.identifier.volume31
dc.identifier.issue5
dc.identifier.arxivid2003.03341
kaust.personTempone, Raul
kaust.grant.numberOSR
kaust.grant.numberURF/1/2281-01-01
kaust.grant.numberURF/1/2584-01-01
dc.date.accepted2021-08-10
refterms.dateFOA2020-03-24T12:54:10Z
kaust.acknowledged.supportUnitCompetitive Research Grants
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)
dc.date.published-online2021-08-30
dc.date.published-print2021-09
dc.date.posted2020-03-06


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
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