Weighting a resampled particle in Sequential Monte Carlo

L. Martino, V. Elvira, F. Louzada

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

Abstract

The Sequential Importance Resampling (SIR) method is the core of the Sequential Monte Carlo (SMC) algorithms (a.k.a., particle filters). In this work, we point out a suitable choice for weighting properly a resampled particle. This observation entails several theoretical and practical consequences, allowing also the design of novel sampling schemes. Specifically, we describe one theoretical result about the sequential estimation of the marginal likelihood. Moreover, we suggest a novel resampling procedure for SMC algorithms called partial resampling, involving only a subset of the current cloud of particles. Clearly, this scheme attenuates the additional variance in the Monte Carlo estimators generated by the use of the resampling.

Original languageEnglish
Title of host publication2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
PublisherIEEE Computer Society
Volume2016-August
ISBN (Electronic)9781467378024
ISBN (Print)978-1-4673-7804-8
DOIs
Publication statusPublished - 24 Aug 2016
Event19th IEEE Statistical Signal Processing Workshop - Palma de Mallorca, Spain
Duration: 25 Jun 201629 Jun 2016

Conference

Conference19th IEEE Statistical Signal Processing Workshop
Abbreviated titleSSP 2016
CountrySpain
CityPalma de Mallorca
Period25/06/1629/06/16

Keywords

  • Importance Sampling
  • Particle Filtering
  • Sequential Importance Resampling
  • Sequential Monte Carlo

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