Marginalized population Monte Carlo

Mónica F. Bugallo, Mingyi Hong, Petar M. Djurić

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

12 Scopus citations

Abstract

Population Monte Carlo is a statistical method that is used for generation of samples approximately from a target distribution. The method is iterative in nature and is based on the principle of importance sampling. In this paper, we show that in problems where some of the parameters are conditionally linear on the remaining parameters, we can improve the computational efficiency of population Monte Carlo by generating samples of the nonlinear parameters only and marginalizing the linear parameters. We demonstrate the marginalized population Monte Carlo on the problem of frequency estimation of closely spaced sinusoids.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Pages2925-2928
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: Apr 19 2009Apr 24 2009

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
CountryTaiwan, Province of China
CityTaipei
Period4/19/094/24/09

Keywords

  • Marginalization
  • Parameter estimation
  • Population Monte Carlo

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