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010 a| 2013933594;

020 a| 9781461463153 (pbk.);

020 a| 1461463157 (pbk.);

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020 z| 1461463165 (ebk.);

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050 00 a| QA273.5b| .R57 2013;

082 00 a| 519.2/32| 23;

100 1 a| Ristic, Branko.=| ^A1248540;

245 10 a| Particle filters for random set modelsh| [electronic resource] /c| Branko Ristic.;

260 a| New York :b| Springer,c| [2013];

300 a| xiv, 174 pages :b| illustrations (some color) ;c| 24 cm;

504 a| Includes bibliographical references and index.;

506 a| Available only to authorized users.;

520 a| 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.--c| Source other than Library of Congress.;

538 a| Mode of access: World Wide Web;

650 0 a| Random sets.=| ^A597746;

650 0 a| Stochastic processes.=| ^A5552;

650 0 a| Estimation theory.=| ^A16116;

650 7 a| Estimation theory.;

650 7 a| Random sets.;

650 7 a| Stochastic processes.;

655 0 a| Electronic books.=| ^A491897;

856 40 z| Full text available from eBooks on EBSCOhostu| https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=577066&scope=site&custid=s5822723&authtype=ip,shib;

947 a| (OCoLC)ocn821217417;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6775866-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 7/19/2026x| EBOOKz| JERESOURCE;

Particle filters for random set models / Branko Ristic.

Author/creator	Ristic, Branko
Format	Electronic
Publication Info	New York : Springer, [2013]
Description	xiv, 174 pages : illustrations (some color) ; 24 cm
Supplemental Content	Full text available from eBooks on EBSCOhost
Subjects	Random sets. Stochastic processes. Estimation theory.

Contents	Introduction -- Background -- Applications Involving Non-standard Measurements -- Multi-Object Particle Filters -- Sensor Control for Random Set BasedParticle Filters -- Multi-Target Tracking -- Advanced Topics.
Abstract	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.-- Source other than Library of Congress.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2013933594
ISBN	9781461463153 (pbk.)
ISBN	1461463157 (pbk.)

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Particle filters for random set models / Branko Ristic.

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