Parallel Markov Chain Monte Carlo Computation for Varying Dimension Signal Analysis

Jing Ye, Andrew Michael Wallace, John S. Thompson

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

Parallel implementation of Markov Chain Monte Carlo (MCMC) algorithms for Bayesian inference has been effective but is usually restricted to the case where the dimension of the parameter vector is fixed. We propose an efficient parallel solution for the varying-dimension problem by constructing multiple within-model MCMC chains and then combining the separate results to analyze the posterior distribution of dimensionality. We aim for parallel speed-up by reducing the length of the burn-in period and the individual chains in comparison with a serial, reversible jump MCMC (RJMCMC) algorithm. The parallel methodology is illustrated with application to a benchmarking, change point problem.
Original languageEnglish
Pages2673-2677
Number of pages5
Publication statusPublished - Aug 2009
Event17th European Signal Processing Conference 2009 - Glasgow, United Kingdom
Duration: 24 Aug 200928 Aug 2009
Conference number: EUSIPCO 2009

Conference

Conference17th European Signal Processing Conference 2009
CountryUnited Kingdom
CityGlasgow
Period24/08/0928/08/09

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