Distributed Particle Metropolis-Hastings Schemes

Luca Martino, Victor Elvira, Gustau Camps-Valls

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce a Particle Metropolis-Hastings algorithm driven by several parallel particle filters. The communication with the central node requires the transmission of only a set of weighted samples, one per filter. Furthermore, the marginal version of the previous scheme, called Distributed Particle Marginal Metropolis-Hastings (DPMMH) method, is also presented. DPMMH can be used for making inference on both a dynamical and static variable of interest. The ergodicity is guaranteed, and numerical simulations show the advantages of the novel schemes.

Original languageEnglish
Title of host publication2018 IEEE Statistical Signal Processing Workshop, SSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages757-761
Number of pages5
ISBN (Electronic)978-1-5386-1571-3
ISBN (Print)9781538615706
DOIs
Publication statusPublished - 29 Aug 2018
Event20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany
Duration: 10 Jun 201813 Jun 2018

Conference

Conference20th IEEE Statistical Signal Processing Workshop, SSP 2018
CountryGermany
CityFreiburg im Breisgau
Period10/06/1813/06/18

Keywords

  • Bayesian inference
  • Monte Carlo
  • Particle Filtering
  • Particle MCMC
  • state-space models

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