Subgradient-based Markov Chain Monte Carlo ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Subgradient-based Markov Chain Monte Carlo particle methods for discrete-time nonlinear filtering
Author(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
Journal title :
Signal Processing
Pages :
532-536
Publisher :
Elsevier
Publication date :
2016-03
ISSN :
0165-1684
English keyword(s) :
Markov chain Monte Carlo methods
High dimensional systems
Compressed sensing
L1 optimisation
Filtering
High dimensional systems
Compressed sensing
L1 optimisation
Filtering
HAL domain(s) :
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]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Popular science :
Non
Collections :
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