On the LP-convergence of a Girsanov theorem based particle filter

Simo Särkkä, Eric Moulines

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

Abstract

We analyze the Lp-convergence of a previously proposed Girsanov theorem based particle filter for discretely observed stochastic differential equation (SDE) models. We prove the convergence of the algorithm with the number of particles tending to infinity by requiring a moment condition and a step-wise initial condition boundedness for the stochastic exponential process giving the likelihood ratio of the SDEs. The practical implications of the condition are illustrated with an Ornstein-Uhlenbeck model and with a non-linear Benes model.

Original languageEnglish
Title of host publicationIEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
PublisherIEEE
Pages3989-3993
Number of pages5
Volume2016-May
ISBN (Print)9781479999880
DOIs
Publication statusPublished - 18 May 2016
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Shanghai, China
Duration: 20 Mar 201625 Mar 2016
Conference number: 41
http://www.icassp2016.org/

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2016
CountryChina
CityShanghai
Period20/03/201625/03/2016
Internet address

Keywords

  • convergence
  • Girsanov theorem
  • particle filter
  • stochastic differential equation

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