This book is about constructing models from experimental data. It
covers a range of topics, from statistical data prediction to
Kalman filtering, from black-box model identification to parameter
estimation, from spectral analysis to predictive control. Written
for graduate students, this textbook offers an approach that has
proven successful throughout the many years during which its author
has taught these topics at his University. The book: Contains
accessible methods explained step-by-step in simple terms Offers an
essential tool useful in a variety of fields, especially
engineering, statistics, and mathematics Includes an overview on
random variables and stationary processes, as well as an
introduction to discrete time models and matrix analysis
Incorporates historical commentaries to put into perspective the
developments that have brought the discipline to its current state
Provides many examples and solved problems to complement the
presentation and facilitate comprehension of the techniques
presented
General
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