Model Identification and Data Analysis

<p>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.</p> <p>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.</p> <p>The book:</p> <ul> <li>Contains accessible methods explained step-by-step in simple terms</li> <li>Offers an essential tool useful in a variety of fields, especially engineering, statistics, and mathematics</li> <li>Includes an overview on random variables and stationary processes, as well as an introduction to discrete time models and matrix analysis</li> <li>Incorporates historical commentaries to put into perspective the developments that have brought the discipline to its current state</li> <li>Provides many examples and solved problems to complement the presentation and facilitate comprehension of the techniques presented</li> </ul>