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

Auteur: Mahsiul Khan
Taal: Engels
Simulation-Based Sequential Bayesian Filtering
  • Engels
  • Paperback
  • 9783843385886
  • januari 2011
  • 124 pagina's
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Samenvatting

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.

Productspecificaties

Inhoud

Taal
Engels
Bindwijze
Paperback
Verschijningsdatum
2011-01-21
Aantal pagina's
124 pagina's
Illustraties
Nee

Betrokkenen

Auteur
Mahsiul Khan
Uitgever
Lap Lambert Academic Publishing

EAN

EAN
9783843385886

Overige kenmerken

Extra groot lettertype
Nee
NUR code
924
Oorspronkelijke releasedatum
2011-01-21
Subtitel
with Rao-Blackwellization applied to Nonlinear Dynamic State Space Models
Thema Subject Code
PHN

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