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hal.structure.identifier
dc.contributor.authorRyder, Robin J.*
dc.date.accessioned2014-12-15T09:34:08Z
dc.date.available2014-12-15T09:34:08Z
dc.date.issued2015
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/14436
dc.language.isoenen
dc.subjectparticle Markov chain Monte Carloen
dc.subjectdynamical systemsen
dc.subjectApproximate Bayesian computationen
dc.subject.ddc519en
dc.titleIntroduction to “Scalable inference for Markov processes with intractable likelihoods” by J. Owen, D. Wilkinson, C. Gillespieen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenApproximate Bayesian computation (ABC) was one of the major themes of MCMSki 2014, with several talks and especially many posters devoted to advances and applications of this method. The approach of Owen, Wilkinson and Gillespie is original, since it uses ABC not to perform direct (approximate) sampling from the posterior, but as an exploratory tool to be used to find a good initialization point for another technique. For the area of application considered (dynamical systems), there as long been a debate about the most useful method between ABC, which gives an approximate answer in a not-unreasonable time, and particle Markov chain Monte Carlo (pMCMC), which is asymptotically exact but easily runs into convergence issues. By using the output of ABC as an initialization for pMCMC, we can get the best of both worlds. This of course generalizes to other MCMC algorithms. The authors have presented convincing results on the Lotka-Volterra model and other dynamical systems.en
dc.relation.isversionofjnlnameStatistics and Computing
dc.relation.isversionofjnlvol25
dc.relation.isversionofjnlissue1
dc.relation.isversionofjnldate2015
dc.relation.isversionofjnlpages143
dc.relation.isversionofdoihttp://dx.doi.org/10.1007/s11222-014-9534-5en
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
hal.author.functionaut


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