The Theory of Linear Prediction
by P. Vaidyanathan
Publisher: Morgan and Claypool Publishers 2008
Number of pages: 198
Linear prediction theory has had a profound impact in the field of digital signal processing. Although prediction is only a part of the more general topics of linear estimation, filtering, and smoothing, this book focuses on linear prediction. The theory of vector linear prediction is explained in considerable detail and so is the theory of line spectral processes.
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by Raghu Raj Bahadur, at al. - IMS
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.
by Victor M. Moreno, Alberto Pigazo - INTECH
An overview of recent developments in Kalman filter theory and their applications in engineering and scientific fields. The book covers recent advances in Kalman filtering theory and applications in electrical engineering and other areas.
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This book uses an index map, a polynomial decomposition, an operator factorization, and a conversion to a filter to develop a very general description of fast algorithms to calculate the discrete Fourier transform. Computer programs are provided.
by Bruce Hajek - University of Illinois at Urbana-Champaign
These notes were written for a graduate course on random processes. Students are assumed to have had a previous course in probability, some familiarity with real analysis and linear algebra, and some familiarity with complex analysis.