Hidden Markov Models: Estimation and Control
by R. J. Elliott, L. Aggoun, J. B. Moore
Publisher: Springer 1995
Number of pages: 373
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. These new and powerful methods are particularly useful in signal processing applications where signal models are only partially known and are in noisy environments.
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by Allen B. Downey - Green Tea Press
'Think DSP: Digital Signal Processing in Python' is an introduction to signal processing and system analysis using a computational approach. The premise of this book is that if you know how to program, you can use that skill to learn other things.
by Michele Basseville, Igor V. Nikiforov - Prentice-Hall
This book presents mathematical tools and techniques for solving change detection problems in wide domains like signal processing, controlled systems and monitoring. The book is intended for engineers and researchers involved in signal processing.
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 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.