Langevin and Hamiltonian based Sequential ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Langevin and Hamiltonian based Sequential MCMC for Efficient Bayesian Filtering in High-dimensional Spaces
Auteur(s) :
Septier, Francois [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Peters, Gareth W. [Auteur]
University College of London [London] [UCL]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Peters, Gareth W. [Auteur]
University College of London [London] [UCL]
Titre de la revue :
IEEE Journal of Selected Topics in Signal Processing
Éditeur :
IEEE
Date de publication :
2016
ISSN :
1932-4553
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]
Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the Sequential Monte Carlo ...
Lire la suite >Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the Sequential Monte Carlo (SMC) algorithm, also known as particle filtering. Nevertheless, this method tends to be inefficient when applied to high dimensional problems. In this paper, we focus on another class of sequential inference methods, namely the Sequential Markov Chain Monte Carlo (SMCMC) techniques, which represent a promising alternative to SMC methods. After providing a unifying framework for the class of SMCMC approaches, we propose novel efficient strategies based on the principle of Langevin diffusion and Hamiltonian dynamics in order to cope with the increasing number of highdimensional applications. Simulation results show that the proposed algorithms achieve significantly better performance compared to existing algorithms.Lire moins >
Lire la suite >Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the Sequential Monte Carlo (SMC) algorithm, also known as particle filtering. Nevertheless, this method tends to be inefficient when applied to high dimensional problems. In this paper, we focus on another class of sequential inference methods, namely the Sequential Markov Chain Monte Carlo (SMCMC) techniques, which represent a promising alternative to SMC methods. After providing a unifying framework for the class of SMCMC approaches, we propose novel efficient strategies based on the principle of Langevin diffusion and Hamiltonian dynamics in order to cope with the increasing number of highdimensional applications. Simulation results show that the proposed algorithms achieve significantly better performance compared to existing algorithms.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
Fichiers
- http://arxiv.org/pdf/1504.05715
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- 1504.05715
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