Parallel metropolis chains with cooperative adaptation

L. Martino, V. Elvira, D. Luengo, F. Louzada

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

Abstract

Monte Carlo methods, such as Markov chain Monte Carlo (MCMC) algorithms, have become very popular in signal processing over the last years. In this work, we introduce a novel MCMC scheme where parallel MCMC chains interact, adapting cooperatively the parameters of their proposal functions. Furthermore, the novel algorithm distributes the computational effort adaptively, rewarding the chains which are providing better performance and, possibly even stopping other ones. These extinct chains can be reactivated if the algorithm considers it necessary. Numerical simulations show the benefits of the novel scheme.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3974-3978
Number of pages5
Volume2016-May
ISBN (Electronic)9781479999880
ISBN (Print)978-1-4799-9987-3
DOIs
Publication statusPublished - 18 May 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: 20 Mar 201625 Mar 2016

Conference

Conference41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Country/TerritoryChina
CityShanghai
Period20/03/1625/03/16

Keywords

  • Adaptive MCMC
  • Bayesian inference
  • cooperative adaptation
  • Interacting Parallel MCMC

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