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dc.contributor.authorRadivojevic, T.
dc.contributor.authorAkhmatskaya, E. 
dc.dateinfo:eu-repo/date/embargoEnd/2020-07-22en_US
dc.date.accessioned2019-08-06T12:50:01Z
dc.date.available2019-08-06T12:50:01Z
dc.date.issued2019-07-22
dc.identifier.issn0960-3174
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1001
dc.description.abstractThe Hamiltonian Monte Carlo (HMC) method has been recognized as a powerful sampling tool in computational statistics. We show that performance of HMC can be significantly improved by incorporating importance sampling and an irreversible part of the dynamics into a chain. This is achieved by replacing Hamiltonians in the Metropolis test with modified Hamiltonians, and a complete momentum update with a partial momentum refreshment. We call the resulting generalized HMC importance sampler—Mix & Match Hamiltonian Monte Carlo (MMHMC). The method is irreversible by construction and further benefits from (i) the efficient algorithms for computation of modified Hamiltonians; (ii) the implicit momentum update procedure and (iii) the multi-stage splitting integrators specially derived for the methods sampling with modified Hamiltonians. MMHMC has been implemented, tested on the popular statistical models and compared in sampling efficiency with HMC, Riemann Manifold Hamiltonian Monte Carlo, Generalized Hybrid Monte Carlo, Generalized Shadow Hybrid Monte Carlo, Metropolis Adjusted Langevin Algorithm and Random Walk Metropolis-Hastings. To make a fair comparison, we propose a metric that accounts for correlations among samples and weights, and can be readily used for all methods which generate such samples. The experiments reveal the superiority of MMHMC over popular sampling techniques, especially in solving high dimensional problems.en_US
dc.description.sponsorshipAgile BioFoundry (http://agilebiofoundry.org) supported by the U.S. Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, through contract DE-AC02-05CH11231 between Lawrence Berkeley National Laboratory and the U.S. Department of Energy.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectBayesian inferenceen_US
dc.subjectMarkov chain Monte Carloen_US
dc.subjectHamiltonian Monte Carloen_US
dc.subjectimportance samplingen_US
dc.subjectmodified Hamiltoniansen_US
dc.titleModified Hamiltonian Monte Carlo for Bayesian Inferenceen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.identifier.doihttps://doi.org/10.1007/s11222-019-09885-x
dc.relation.publisherversionhttps://doi.org/10.1007/s11222-019-09885-xen_US
dc.relation.projectIDES/1PE/SEV-2017-0718en_US
dc.relation.projectIDES/1PE/MTM2016-76329-Ren_US
dc.relation.projectIDEUS/BERC/BERC.2018-2021en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleStatistics and Computingen_US


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