Interacting multiple -- Try algorithms with different proposal distributions

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Show simple item record Casarin, Roberto Craiu, Radu Leisen, Fabrizio
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística 2011-04-04T15:40:38Z 2011-04-04T15:40:38Z 2011-03
dc.description.abstract We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing different proposal distributions in the multipletry generation step. We exploit the structure of the MTM algorithm with different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods and builds connections with the rapidly expanding world of adaptive MCMC. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. We provide numerical studies which show that the new algorithm can perform better than the basic MTM algorithm and that the interaction mechanism allows the IMTM to efficiently explore the state space.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries 11-02
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Interacting Monte Carlo
dc.subject.other Markov chain Monte Carlo
dc.subject.other Multiple-try Metropolis
dc.subject.other Population Monte Carlo
dc.title Interacting multiple -- Try algorithms with different proposal distributions
dc.type workingPaper
dc.subject.eciencia Estadística
dc.rights.accessRights openAccess
dc.identifier.uxxi DT/0000000934
dc.identifier.repec ws110402
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