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＜E-Book＞
Random Iterative Models / by Marie Duflo
(Stochastic Modelling and Applied Probability. ISSN:01724568 ; 34)

Edition	1st ed. 1997.
Publisher	(Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer)
Year	1997
Size	XV, 385 p : online resource
Authors	*Duflo, Marie author SpringerLink (Online service)
Subjects	LCSH:Probabilities LCSH:Algorithms LCSH:Mathematics FREE:Probability Theory and Stochastic Processes FREE:Algorithms FREE:Mathematics, general
Notes	I. Sources of Recursive Methods -- 1. Traditional Problems -- 2. Rate of Convergence -- 3. Current Problems -- II. Linear Models -- 4. Causality and Excitation -- 5. Linear Identification and Tracking -- III. Nonlinear Models -- 6. Stability -- 7. Nonlinear Identification and Control -- IV. Markov Models -- 8. Recurrence -- 9. Learning The recent development of computation and automation has lead to quick advances in the theory and practice of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, orgainizing multiaccess broadcast channels, self-learning of neural networks ...). This book provides a wide-angle view of those methods: stochastic approximation, linear and non-linear models, controlled Markov chains, estimation and adaptive control, learning ... Mathematicians familiar with the basics of Probability and Statistics will find here a self-contained account of many approaches to those theories, some of them classical, some of them leading up to current and future research. Each chapter can form the core material for a course of lectures. Engineers having to control complex systems can discover new algorithms with good performances and reasonably easy computation HTTP:URL=https://doi.org/10.1007/978-3-662-12880-0

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