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SMC with Adaptive Resampling: Large Sample Asymptotics

Élise Arnaud 1 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 longstanding problem in sequential Monte Carlo (SMC) is to mathematically prove the popular belief that resampling does improve the performance of the estimation (this of course is not always true, and the real question is to clarify classes of problems where resampling helps). A more pragmatic answer to the problem is to use adaptive procedures that have been proposed on the basis of heuristic considerations, where resampling is performed only when it is felt necessary, i.e. when some criterion (effective number of particles, entropy of the sample, etc.) reaches some prescribed threshold. It still remains to mathematically prove the efficiency of such adaptive procedures.
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Submitted on : Friday, October 2, 2009 - 2:09:48 PM
Last modification on : Monday, February 7, 2022 - 1:54:01 PM
Long-term archiving on: : Wednesday, June 16, 2010 - 12:18:38 AM




Élise Arnaud, François Le Gland. SMC with Adaptive Resampling: Large Sample Asymptotics. SSP 2009 - 15th IEEE/SP Workshop on Statistical Signal Processing, Aug 2009, Cardiff, United Kingdom. pp.481-484, ⟨10.1109/SSP.2009.5278533⟩. ⟨inria-00421619⟩



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