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Combined use of importance weights and resampling weights in sequential Monte Carlo methods

François Le Gland 1
1 ASPI - Applications of interacting particle systems to statistics
UR1 - Université de Rennes 1, Inria Rennes – Bretagne Atlantique , CNRS - Centre National de la Recherche Scientifique : UMR6074
Abstract : A particle approximation of Feynman-Kac distributions is presented here, that combines SIS and SIR algorithms in the sense that only a part of the importance weights is used for resampling, and two different approaches are proposed to analyze its performance. The first approach is based on a representation in terms of path-space distributions, and could be used to analyze the joint particle approximation of distributions for a reference model and several alternate models at the same time. The second approach, which is of independent interest and seems very promising, is based on a representation in terms of a multiplicative functional, and could be used to analyze particle approximation with adaptive resampling schemes.
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https://hal.inria.fr/hal-00912067
Contributor : Francois Le Gland <>
Submitted on : Sunday, December 1, 2013 - 11:37:56 PM
Last modification on : Wednesday, May 16, 2018 - 11:23:02 AM

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François Le Gland. Combined use of importance weights and resampling weights in sequential Monte Carlo methods. ESAIM: Proceedings, EDP Sciences, 2007, Conference Oxford sur les méthodes de Monte Carlo séquentielles, 19, pp.85-100. ⟨10.1051/proc:071912⟩. ⟨hal-00912067⟩

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