Random Iterative Models

Marie Duflo

ISBN 10: 3540571000 / ISBN 13: 9783540571001

Published by Springer-Verlag, New York, NY, 1997

About this Item

First English Language Edition (1997) ; First Printing (unstated). Very Near Fine: shows mild sun-blanching to the yellow background field of the back-strip (the black & white titles thereon remain bold and clearly legible) ; else flawless; the binding is square and secure; the text is clean. Free of creased or dog-eared pages in the text. Free of any underlining, hi-lighting or marginalia or marks in the text. Free of ownership names, dates, addresses, notations, inscriptions, stamps, or labels. A handsome, nearly-new copy, structurally sound and tightly bound, showing a single unobtrusive cosmetic imperfection. NOT a Remainder, Book-Club, or Ex-Library. Large 8vo (9.5 x 6.35 x 1.15 inches). Translated from the French by Stephen S. Wilson. Language: English. Weight: 25 Ounces. Yellow boards with black and white titles and black & blue designs at the front panel and black & white titles at the back-strip. First published in French (undated). Hardcover: No DJ as issued. An up-to-date, self-contained review of a wide range of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, organizing multi-access broadcast channels, self-learning of neural networks .). Suitable for mathematicians (researchers and also students) and engineers. ; Applications of Mathematics; Vol. 34; Large 8vo 9" - 10" tall; viii, 241 pages.

Seller Inventory # 59385

Bibliographic Details

• Title: Random Iterative Models
• Publisher: Springer-Verlag, New York, NY
• Publication Date: 1997
• Language: English
• ISBN 10: 3540571000
• ISBN 13: 9783540571001
• Binding: Hardcover
• Edition: First American Edition; First Printing.
• Condition: Near Fine

About this title

Synopsis

An up-to-date, self-contained review of a wide range of recursive methods for stabilization, identification and control of complex stochastic models (guiding a rocket or a plane, organizing multi-access broadcast channels, self-learning of neural networks ...). Suitable for mathematicians (researchers and also students) and engineers.

From the Back Cover

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.