simulation based sequential bayesian filtering de khan mahsiul (6 résultats)

Taschenbuch. Etat : Neu. Simulation-Based Sequential Bayesian Filtering | with Rao-Blackwellization applied to Nonlinear Dynamic State Space Models | Mahsiul Khan | Taschenbuch | 124 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783843385886 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 124 pp. Englisch.

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Khan MahsiulMahsiul 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, .

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 124 pp. Englisch.

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.