Subgradient-based Markov Chain Monte Carlo ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Subgradient-based Markov Chain Monte Carlo particle methods for discrete-time nonlinear filtering
Auteur(s) :
Carmi, Avishy [Auteur]
Department of Mechanical Engineering [Beer-Sheva]
Mihaylova, Lyudmila [Auteur]
University of Sheffield [Sheffield]
Septier, Francois [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Institut TELECOM/TELECOM Lille1
Department of Mechanical Engineering [Beer-Sheva]
Mihaylova, Lyudmila [Auteur]
University of Sheffield [Sheffield]
Septier, Francois [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Institut TELECOM/TELECOM Lille1
Titre de la revue :
Signal Processing
Pagination :
532-536
Éditeur :
Elsevier
Date de publication :
2016-03
ISSN :
0165-1684
Mot(s)-clé(s) en anglais :
Markov chain Monte Carlo methods
High dimensional systems
Compressed sensing
L1 optimisation
Filtering
High dimensional systems
Compressed sensing
L1 optimisation
Filtering
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Calcul [stat.CO]
Statistiques [stat]/Méthodologie [stat.ME]
Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Calcul [stat.CO]
Statistiques [stat]/Méthodologie [stat.ME]
Résumé en anglais : [en]
This work shows how a carefully designed instrumental distribution can improve the performance of a Markov chain Monte Carlo (MCMC) filter for systems with a high state dimension. We propose a special subgradient-based ...
Lire la suite >This work shows how a carefully designed instrumental distribution can improve the performance of a Markov chain Monte Carlo (MCMC) filter for systems with a high state dimension. We propose a special subgradient-based kernel from which candidate moves are drawn. This facilitates the implementation of the filtering algorithm in high dimensional settings using a remarkably small number of particles. We demonstrate our approach in solving a nonlinear non-Gaussian high-dimensional problem in comparison with a recently developed block particle filter and over a dynamic compressed sensing (l1 constrained) algorithm. The results show high estimation accuracy.Lire moins >
Lire la suite >This work shows how a carefully designed instrumental distribution can improve the performance of a Markov chain Monte Carlo (MCMC) filter for systems with a high state dimension. We propose a special subgradient-based kernel from which candidate moves are drawn. This facilitates the implementation of the filtering algorithm in high dimensional settings using a remarkably small number of particles. We demonstrate our approach in solving a nonlinear non-Gaussian high-dimensional problem in comparison with a recently developed block particle filter and over a dynamic compressed sensing (l1 constrained) algorithm. The results show high estimation accuracy.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
Fichiers
- http://eprints.whiterose.ac.uk/91024/1/Subgradient_MCMC_SigProcwith_Figures.pdf
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- Subgradient_MCMC_SigProcwith_Figures.pdf
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