An improved SIR-based sequential Monte Carlo algorithm

Abstract : Sequential Monte Carlo (SMC) algorithms are based on importance sampling (IS) techniques. Resampling has been introduced as a tool for fighting the weight degeneracy problem. However, for a fixed sample size N, the resampled particles are dependent, are not drawn exactly from the target distribution, nor are weighted properly. In this paper, we revisit the resampling mechanism and propose a scheme where the resampled particles are (conditionally) independent and weighted properly. We validate our results via simulations.
Type de document :
Communication dans un congrès
2016 IEEE Workshop on Statistical Signal Processing (SSP 16), Jun 2016, Palma de Mallorca, Spain. 2016
Liste complète des métadonnées

https://hal-imt.archives-ouvertes.fr/hal-01359095
Contributeur : François Septier <>
Soumis le : jeudi 1 septembre 2016 - 17:54:31
Dernière modification le : vendredi 13 avril 2018 - 01:23:14

Identifiants

  • HAL Id : hal-01359095, version 1

Citation

Roland Lamberti, Yohan Petetin, François Septier, François Desbouvries. An improved SIR-based sequential Monte Carlo algorithm. 2016 IEEE Workshop on Statistical Signal Processing (SSP 16), Jun 2016, Palma de Mallorca, Spain. 2016. 〈hal-01359095〉

Partager

Métriques

Consultations de la notice

237