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dc.contributor.authorLatz, Jonas
dc.contributor.authorMadrigal-Cianci, Juan P.
dc.contributor.authorNobile, Fabio
dc.contributor.authorTempone, Raul
dc.date.accessioned2020-03-24T12:53:42Z
dc.date.available2020-03-24T12:53:42Z
dc.date.issued2020-03-06
dc.identifier.urihttp://hdl.handle.net/10754/662284
dc.description.abstractIn the current work we present two generalizations of the Parallel Tempering algorithm, inspired by the so-called continuous-time Infinite Swapping algorithm. Such a method, found its origins in the molecular dynamics community, and can be understood as the limit case of the continuous-time Parallel Tempering algorithm, where the (random) time between swaps of states between two parallel chains goes to zero. Thus, swapping states between chains occurs continuously. In the current work, we extend this idea to the context of time-discrete Markov chains and present two Markov chain Monte Carlo algorithms that follow the same paradigm as the continuous-time infinite swapping procedure. We analyze the convergence properties of such discrete-time algorithms in terms of their spectral gap, and implement them to sample from different target distributions. Numerical results show that the proposed methods significantly improve over more traditional sampling algorithms such as Random Walk Metropolis and (traditional) Parallel Tempering.
dc.description.sponsorshipThis 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 Center for Advance Modeling Science (CADMOS) and the Swiss Data Science Center (SDSC) Grant p18-09.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2003.03341
dc.rightsArchived with thanks to arXiv
dc.titleGeneralized Parallel Tempering on Bayesian Inverse Problems
dc.typePreprint
dc.contributor.departmentApplied Mathematics and Computational Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentStochastic Numerics Research Group
dc.eprint.versionPre-print
dc.contributor.institutionDepartment of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.
dc.contributor.institutionSB-MATH-CSQI, Ecole Polytechnique F´ed´erale de Lausanne, Lausanne Switzerland
dc.identifier.arxivid2003.03341
kaust.personTempone, Raul
kaust.grant.numberURF/1/2281-01-01
kaust.grant.numberURF/1/2584-01-01
refterms.dateFOA2020-03-24T12:54:10Z
kaust.acknowledged.supportUnitCompetitive Research
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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