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Bayesian Bounds For Parameter Estimation And Nonlinear Filtering Tracking 1st Edition Harry L Van Trees

  • SKU: BELL-2474896
Bayesian Bounds For Parameter Estimation And Nonlinear Filtering Tracking 1st Edition Harry L Van Trees
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Bayesian Bounds For Parameter Estimation And Nonlinear Filtering Tracking 1st Edition Harry L Van Trees instant download after payment.

Publisher: Wiley-IEEE Press
File Extension: PDF
File size: 235.53 MB
Pages: 960
Author: Harry L. Van Trees, Kristine L. Bell
ISBN: 9780470120958, 0470120959
Language: English
Year: 2007
Edition: 1

Product desciption

Bayesian Bounds For Parameter Estimation And Nonlinear Filtering Tracking 1st Edition Harry L Van Trees by Harry L. Van Trees, Kristine L. Bell 9780470120958, 0470120959 instant download after payment.

The first comprehensive development of Bayesian Bounds for parameter estimation and nonlinear filtering/trackingBayesian estimation plays a central role in many signal processing problems encountered in radar, sonar, communications, seismology, and medical diagnosis. There are often highly nonlinear problems for which analytic evaluation of the exact performance is intractable. A widely used technique is to find bounds on the performance of any estimator and compare the performance of various estimators to these bounds.This book provides a comprehensive overview of the state of the art in Bayesian Bounds. It addresses two related problems: the estimation of multiple parameters based on noisy measurements and the estimation of random processes, either continuous or discrete, based on noisy measurements.An extensive introductory chapter provides an overview of Bayesian estimation and the interrelationship and applicability of the various Bayesian Bounds for both static parameters and random processes. It provides the context for the collection of papers that are included.This book will serve as a comprehensive reference for engineers and statisticians interested in both theory and application. It is also suitable as a text for a graduate seminar or as a supplementary reference for an estimation theory course.

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