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| Research article summary (published 22 Sep 2009): |
Implicit sampling for particle filters.
Full Abstract
We present a particle-based nonlinear filtering scheme, related to recent work on chainless Monte Carlo, designed to focus particle paths sharply so that fewer particles are required. The main features of the scheme are a representation of each new probability density function by means of a set of functions of Gaussian variables (a distinct function for each particle and step) and a resampling based on normalization factors and Jacobians. The construction is demonstrated on a standard, ill-conditioned test problem.
Author information
Author/s: Chorin, Alexandre J (AJ); Tu, Xuemin (X);
Affiliation: Department of Mathematics, University of California and Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA. chorin(-atsign-)math.berkeley.edu
Journal and publication information
Publication Type: Journal Article; Research Support, U.S. Gov't, Non-P.H.S.
Journal: Proceedings of the National Academy of Sciences of the United States of America (Proc Natl Acad Sci U S A), published in United States. (Language: eng)
Reference: 2009-Oct; vol 106 (issue 41) : pp 17249-54
Dates: Created 2009/10/15; Completed 2009/11/03; Revised 2009/11/06;
PMID: 19805147, status: MEDLINE (last retrieval date: 11/9/2009, IMS Date: )
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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