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dc.contributor.authorRobert, Christian P.
dc.contributor.authorWu, Changye
dc.contributor.authorStoehr, Julien
dc.date.accessioned2019-04-23T11:10:41Z
dc.date.available2019-04-23T11:10:41Z
dc.date.issued2019
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/18740
dc.language.isoenen
dc.subjectAcceleration methodsen
dc.subjectNo-U-Turn Sampleren
dc.subject.ddc621.3en
dc.titleFaster Hamiltonian Monte Carlo by Learning Leapfrog Scaleen
dc.typeDocument de travail / Working paper
dc.description.abstractenHamiltonian Monte Carlo samplers have become standard algorithms for MCMC implementations, as opposed to more basic versions, but they still require some amount of tuning and calibration. Exploiting the U-turn criterion of the NUTS algorithm (Hoffman and Gelman, 2014), we propose a version of HMC that relies on the distribution of the integration time of the associated leapfrog integrator. Using in addition the primal-dual averaging method for tuning the step size of the integrator, we achieve an essentially calibration free version of HMC. When compared with the original NUTS on several benchmarks, this algorithm exhibits a significantly improved efficiency.en
dc.publisher.cityParisen
dc.identifier.citationpages18en
dc.relation.ispartofseriestitleCahier de recherche CEREMADE, Université Paris-Dauphineen
dc.subject.ddclabelTraitement du signalen
dc.identifier.citationdate2019
dc.description.ssrncandidatenonen
dc.description.halcandidatenonen
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.date.updated2019-03-26T13:06:38Z
hal.person.labIds60
hal.person.labIds60
hal.person.labIds442774


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