A double proposal normalized importance ...
Document type :
Communication dans un congrès avec actes
DOI :
Title :
A double proposal normalized importance sampling estimator
Author(s) :
Lamberti, Roland [Auteur]
Traitement de l'Information Pour Images et Communications [TIPIC-SAMOVAR]
Centre National de la Recherche Scientifique [CNRS]
Communications, Images et Traitement de l'Information [TSP - CITI]
Petetin, Yohan [Auteur]
Centre National de la Recherche Scientifique [CNRS]
Communications, Images et Traitement de l'Information [TSP - CITI]
Traitement de l'Information Pour Images et Communications [TIPIC-SAMOVAR]
Septier, François [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Desbouvries, François [Auteur]
Centre National de la Recherche Scientifique [CNRS]
Communications, Images et Traitement de l'Information [TSP - CITI]
Traitement de l'Information Pour Images et Communications [TIPIC-SAMOVAR]
Traitement de l'Information Pour Images et Communications [TIPIC-SAMOVAR]
Centre National de la Recherche Scientifique [CNRS]
Communications, Images et Traitement de l'Information [TSP - CITI]
Petetin, Yohan [Auteur]
Centre National de la Recherche Scientifique [CNRS]
Communications, Images et Traitement de l'Information [TSP - CITI]
Traitement de l'Information Pour Images et Communications [TIPIC-SAMOVAR]
Septier, François [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Desbouvries, François [Auteur]
Centre National de la Recherche Scientifique [CNRS]
Communications, Images et Traitement de l'Information [TSP - CITI]
Traitement de l'Information Pour Images et Communications [TIPIC-SAMOVAR]
Conference title :
SSP 2018: IEEE Statistical Signal Processing Workshop
City :
Freiburg
Country :
Allemagne
Start date of the conference :
2018-06-10
Book title :
Proceedings SSP 2018: IEEE Statistical Signal Processing Workshop
Publisher :
IEEE Computer Society
Publication date :
2018
English keyword(s) :
Variance minimization
Importance sampling
Monte Carlo integration
Importance sampling
Monte Carlo integration
HAL domain(s) :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
English abstract : [en]
Monte Carlo methods rely on random sampling to compute and approximate expectations of interest in signal processing. Among Monte Carlo methods for integration, Importance Sampling is a variance reduction technique which ...
Show more >Monte Carlo methods rely on random sampling to compute and approximate expectations of interest in signal processing. Among Monte Carlo methods for integration, Importance Sampling is a variance reduction technique which consists in sampling from an importance distribution which is not necessary the original target distribution. The performance of the resulting estimate is strongly related to the critical choice of such an important distribution. In this paper we revisit the rationale of the normalized importance sampling technique and show that it is possible to improve the classical importance sampling estimate by approximating the expectation of interest via two importance distributions. The choice of these two importance distributions is optimized w.r.t. the variance of the final estimate. Our results are validated via numerical simulationsShow less >
Show more >Monte Carlo methods rely on random sampling to compute and approximate expectations of interest in signal processing. Among Monte Carlo methods for integration, Importance Sampling is a variance reduction technique which consists in sampling from an importance distribution which is not necessary the original target distribution. The performance of the resulting estimate is strongly related to the critical choice of such an important distribution. In this paper we revisit the rationale of the normalized importance sampling technique and show that it is possible to improve the classical importance sampling estimate by approximating the expectation of interest via two importance distributions. The choice of these two importance distributions is optimized w.r.t. the variance of the final estimate. Our results are validated via numerical simulationsShow less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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