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Bayesian Estimation and Tracking – A Practical Guide Hardback - 2012
by Haug, Anton J
- New
- Hardback
A$297.32
A$48.06
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Details
- Title Bayesian Estimation and Tracking – A Practical Guide
- Author Haug, Anton J
- Binding Hardback
- Edition Edition:1
- Condition New
- Pages 400
- Volumes 1
- Language ENG
- Publisher John Wiley & Sons Inc
- Publication date 2012
- Illustrated Yes
- Features Bibliography, Illustrated, Index, Table of Contents
- Bookseller's Inventory # x-0470621702
- ISBN 9780470621707 / 0470621702
- Weight 1.5 lbs (0.68 kg)
- Dimensions 9.3 x 6.1 x 0.9 in (23.62 x 15.49 x 2.29 cm)
- Category Mathematics
- Library of Congress subjects Bayesian statistical decision theory, Estimation theory
- Library of Congress Catalogue Number 2011044308
- Dewey Decimal Code 519.542
- Quantity available 2
About Revaluation Books Devon, United Kingdom
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General bookseller of both fiction and non-fiction.
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From the publisher
From the rear cover
A practical approach to estimating and tracking dynamic systems in real-world applications
Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noises.
Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of each estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand.
Case studies are employed to showcase applications of the discussed topics. In addition, the book supplies block diagrams for each algorithm, allowing readers to develop their own MATLAB(R) toolbox of estimation methods.
Bayesian Estimation and Tracking is an excellent book for courses on estimation and tracking methods at the graduate level. The book also serves as a valuable reference for research scientists, mathematicians, and engineers seeking a deeper understanding of the topics.
