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Introduction to Bayesian tracking an...

Stone, Lawrence D.

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Introduction to Bayesian tracking and particle filters

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to Bayesian tracking and particle filters/ by Lawrence D. Stone, Roy L. Streit, Stephen L. Anderson.
Author:	Stone, Lawrence D.
other author:	Streit, Roy L.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	1 online resource (vi, 118 p.) :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Bayesian Single Target Tracking -- Bayesian Particle Filtering -- Simple Multiple Target Tracking -- Intensity Filters.
Contained By:	Springer Nature eBook
Subject:	Bayesian statistical decision theory. -
Online resource:	https://doi.org/10.1007/978-3-031-32242-6
ISBN:	9783031322426

Introduction to Bayesian tracking and particle filters
Stone, Lawrence D.

Introduction to Bayesian tracking and particle filters[electronic resource] /by Lawrence D. Stone, Roy L. Streit, Stephen L. Anderson. - Cham :Springer International Publishing :2023. - 1 online resource (vi, 118 p.) :ill., digital ;24 cm. - Studies in big data,v. 1262197-6511 ;. - Studies in big data ;v. 126..

Introduction -- Bayesian Single Target Tracking -- Bayesian Particle Filtering -- Simple Multiple Target Tracking -- Intensity Filters.

This book provides a quick but insightful introduction to Bayesian tracking and particle filtering for a person who has some background in probability and statistics and wishes to learn the basics of single-target tracking. It also introduces the reader to multiple target tracking by presenting useful approximate methods that are easy to implement compared to full-blown multiple target trackers. The book presents the basic concepts of Bayesian inference and demonstrates the power of the Bayesian method through numerous applications of particle filters to tracking and smoothing problems. It emphasizes target motion models that incorporate knowledge about the target's behavior in a natural fashion rather than assumptions made for mathematical convenience. The background provided by this book allows a person to quickly become a productive member of a project team using Bayesian filtering and to develop new methods and techniques for problems the team may face.

ISBN: 9783031322426

Standard No.: 10.1007/978-3-031-32242-6doiSubjects--Topical Terms:

551404
Bayesian statistical decision theory.

LC Class. No.: QA279.5

Dewey Class. No.: 519.542

Introduction to Bayesian tracking and particle filters
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245 1 0 $a Introduction to Bayesian tracking and particle filters $h [electronic resource] / $c by Lawrence D. Stone, Roy L. Streit, Stephen L. Anderson.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a 1 online resource (vi, 118 p.) : $b ill., digital ; $c 24 cm.
490 1 $a Studies in big data, $x 2197-6511 ; $v v. 126
505 0 $a Introduction -- Bayesian Single Target Tracking -- Bayesian Particle Filtering -- Simple Multiple Target Tracking -- Intensity Filters.
520 $a This book provides a quick but insightful introduction to Bayesian tracking and particle filtering for a person who has some background in probability and statistics and wishes to learn the basics of single-target tracking. It also introduces the reader to multiple target tracking by presenting useful approximate methods that are easy to implement compared to full-blown multiple target trackers. The book presents the basic concepts of Bayesian inference and demonstrates the power of the Bayesian method through numerous applications of particle filters to tracking and smoothing problems. It emphasizes target motion models that incorporate knowledge about the target's behavior in a natural fashion rather than assumptions made for mathematical convenience. The background provided by this book allows a person to quickly become a productive member of a project team using Bayesian filtering and to develop new methods and techniques for problems the team may face.
650 0 $a Bayesian statistical decision theory. $3 551404
650 0 $a Particle methods (Numerical analysis) $3 812337
650 1 4 $a Data Engineering. $3 3409361
650 2 4 $a Bayesian Inference. $3 3386929
650 2 4 $a Big Data. $3 3134868
700 1 $a Streit, Roy L. $3 1244444
700 1 $a Anderson, Stephen Lynn. $3 3634304
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in big data ; $v v. 126. $3 3634305
856 4 0 $u https://doi.org/10.1007/978-3-031-32242-6
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)