SMC with Adaptive Resampling: Large Sample Asymptotics
Résumé
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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Arnaud_Legland_09.pdf (395.46 Ko)
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smc.jpg (10.22 Ko)
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poster-ssp09-final.pdf (97.74 Ko)
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Format | Autre |
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