Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking

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Van Trees, Harry L./ Bell, Kristine L. 作者
Wiley-IEEE Press
译者
2007-8-31 出版日期
951 页数
USD 143.95 价格
Hardcover
丛书系列
9780470120958 图书编码

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Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



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Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking 在线电子书 图书描述

The first comprehensive development of Bayesian Bounds for parameter estimation and nonlinear filtering/tracking Bayesian 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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