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Geometric ergodicity of Rao and Teh's algorithm for Markov jump processes and CTBNs

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cris.lastimport.scopus2024-02-12T19:51:01Z
dc.abstract.enRao and Teh (2012, 2013) introduced an efficient MCMC algorithm for sampling from the posterior distribution of a hidden Markov jump process. The algorithm is based on the idea of sampling virtual jumps. In the present paper we show that the Markov chain generated by Rao and Teh’s algorithm is geometrically ergodic. To this end we establish a geometric drift condition towards a small set. A similar result is also proved for a special version of the algorithm, used for probabilistic inference in Continuous Time Bayesian Networks.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorMiasojedow, Błażej
dc.contributor.authorNiemiro, Wojciech
dc.date.accessioned2024-01-25T02:06:00Z
dc.date.available2024-01-25T02:06:00Z
dc.date.issued2017
dc.description.financeNie dotyczy
dc.description.number2
dc.description.volume11
dc.identifier.doi10.1214/17-EJS1348
dc.identifier.issn1935-7524
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/107878
dc.identifier.weblinkhttp://dx.doi.org/10.1214/17-EJS1348
dc.languageeng
dc.pbn.affiliationmathemathics
dc.relation.ispartofElectronic Journal of Statistics
dc.relation.pages4629-4648
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.subject.enContinuous time Markov processes
dc.subject.enMCMC
dc.subject.enhidden Markov models
dc.subject.enposterior sampling
dc.subject.engeometric ergodicity
dc.subject.endrift condition
dc.subject.ensmall set
dc.subject.encontinuous time Bayesian network
dc.titleGeometric ergodicity of Rao and Teh's algorithm for Markov jump processes and CTBNs
dc.typeJournalArticle
dspace.entity.typePublication