Detection of Abrupt Changes: Theory and Application
by Michele Basseville, Igor V. Nikiforov
Publisher: Prentice-Hall 1993
Number of pages: 469
This book presents mathematical tools and techniques for solving change detection problems in wide domains like signal processing, controlled systems and monitoring. The book covers a wide class of stochastic processes, from scalar independent observations to multidimensional dependent ARMA and state-space models, the properties of the algorithms for statistical change detection, tuning and optimizing change detection in real-world applications. The book is intended for engineers and researchers involved in signal processing, and others.
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by Javier Prieto Tejedor (ed.) - InTech
This book takes a look at both theoretical foundations and practical implementations of Bayesian inference. It is intended as an introductory guide for the application of Bayesian inference in the fields of life sciences, engineering, and economics.
by John C. Nash - Marcel Dekker Inc
This book and software collection is intended to help scientists, engineers and statisticians in their work. We have collected various software tools for nonlinear parameter estimation, along with representative example problems.
by R. J. Elliott, L. Aggoun, J. B. Moore - Springer
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.
by Sophocles J. Orfanidis
In this edition the emphasis is on real-time adaptive signal processing, eigenvector methods of spectrum estimation, and parallel processor implementations of optimum filtering and prediction algorithms, and including several new developments.