Parallel interacting Markov adaptive importance sampling

Luca Martino, Victor Elvira, David Luengo, Jukka Corander

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

Abstract

Monte Carlo (MC) methods are widely used for statistical inference in signal processing applications. A well-known class of MC methods is importance sampling (IS) and its adaptive extensions. In this work, we introduce an iterated importance sampler using a population of proposal densities, which are adapted according to an MCMC technique over the population of location parameters. The novel algorithm provides a global estimation of the variables of interest iteratively, using all the samples weighted according to the deterministic mixture scheme. Numerical results, on a multi-modal example and a localization problem in wireless sensor networks, show the advantages of the proposed schemes.

Original languageEnglish
Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages499-503
Number of pages5
ISBN (Electronic)9780992862633
DOIs
Publication statusPublished - 22 Dec 2015
Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Duration: 31 Aug 20154 Sep 2015

Conference

Conference23rd European Signal Processing Conference, EUSIPCO 2015
CountryFrance
CityNice
Period31/08/154/09/15

Keywords

  • Adaptive importance sampling
  • Bayesian inference
  • MCMC methods
  • parallel chains

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