Simulation-Based Sequential Bayesian Filtering: with Rao-Blackwellization applied to Nonlinear Dynamic State Space Models - Couverture souple

Khan, Mahsiul

 
9783843385886: Simulation-Based Sequential Bayesian Filtering: with Rao-Blackwellization applied to Nonlinear Dynamic State Space Models
Couverture souple
ISBN 10 : 3843385882 ISBN 13 : 9783843385886
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

Stochastic models are used to describe many real world random processes which necessitate the extraction of hidden (unobserved) states (signals) from noisy observable (measured) outputs. We consider a class of nonlinear dynamic state space models which contain conditionally linear and unknown static parameters. For tracking the a posteriori distribution of the hidden states of this type of models, one can apply particle filtering, which is an increasingly popular method in many fields of science and engineering. It is based on the Bayesian methodology and approximations of the distributions of interest with random measures composed of samples (particles) from the space of the states and weights associated to the particles. Particle filtering performs tracking of the desired distributions as new observations are made by modifying the random measure, that is, the particles and the weights. We address the application of particle filtering with the use of the Rao-Blackwellization principle. Rao-Blackwellization reduces the variance of estimators, and it is based on the Rao-Blackwell theorem.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Stochastic models are used to describe many real world random processes which necessitate the extraction of hidden (unobserved) states (signals) from noisy observable (measured) outputs. We consider a class of nonlinear dynamic state space models which contain conditionally linear and unknown static parameters. For tracking the a posteriori distribution of the hidden states of this type of models, one can apply particle filtering, which is an increasingly popular method in many fields of science and engineering. It is based on the Bayesian methodology and approximations of the distributions of interest with random measures composed of samples (particles) from the space of the states and weights associated to the particles. Particle filtering performs tracking of the desired distributions as new observations are made by modifying the random measure, that is, the particles and the weights. We address the application of particle filtering with the use of the Rao-Blackwellization principle. Rao-Blackwellization reduces the variance of estimators, and it is based on the Rao-Blackwell theorem.

Biographie de l'auteur

Mahsiul Khan received his BE in Electrical Engineering from the Stony Brook University, New York, USA in 1995. He also received his MS, MPhil and PhD in Applied Mathematics and Statistics from the Stony Brook University, New York, USA in 2002, 2005 and 2009 respectively.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3843385882
ISBN 13 
9783843385886
Reliure
Broché
Nombre de pages
124
Coordonnées du fabricant
non disponible
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