R. R. Bahadur's Lectures on the Theory of Estimation
by Raghu Raj Bahadur, at al.
Publisher: IMS 2002
Number of pages: 82
In this volume the author covered what should be standard topics in a course of parametric estimation: Bayes estimates, unbiased estimation, Fisher information, Cramer-Rao bounds, and the theory of maximum likelihood estimation.
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by William A. Gardner - McGraw-Hill
A first course on random processes for graduate engineering and science students, particularly those with an interest in the analysis and design of signals and systems. The book includes detailed coverage of minimum-mean-squared-error estimation.
by Brad Osgood - Stanford University
This text is appropriate for science and engineering students. Topics include: Periodicity and Fourier series; The Fourier transform and its basic properties; Convolution and its applications; Distributions and their Fourier transforms; etc.
by B.D.O. Anderson, J.B. Moore - Prentice-Hall
This graduate-level text augments and extends studies of signal processing, particularly in regard to communication systems and digital filtering theory. Topics include filtering, linear systems, and estimation; the discrete-time Kalman filter; etc.
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The aim of this book is to present graduate students with a thorough survey of reference probability models and their applications to optimal estimation and control. Readers are assumed to have basic grounding in probability and systems theory.