Particle Filters for Random Set Models

"Particle Filters for Random Set Models" presents coverage of state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based  on the Monte Carl...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ristic, Branko (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Temas:
Signal processing.
Computer science-Mathematics.
Mathematical statistics.
Artificial intelligence.
Computational intelligence.
Signal, Speech and Image Processing .
Mathematical Applications in Computer Science.
Probability and Statistics in Computer Science.
Artificial Intelligence.
Computational Intelligence.
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