Particle Filters for Random Set Models

Nonfiction, Science & Nature, Mathematics, Applied, Technology, Electronics
Cover of the book Particle Filters for Random Set Models by Branko Ristic, Springer New York
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Author: Branko Ristic ISBN: 9781461463160
Publisher: Springer New York Publication: April 15, 2013
Imprint: Springer Language: English
Author: Branko Ristic
ISBN: 9781461463160
Publisher: Springer New York
Publication: April 15, 2013
Imprint: Springer
Language: English

This book discusses 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 Carlo statistical method. Although the resulting  algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book discusses 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 Carlo statistical method. Although the resulting  algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.

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