Approximation and Weak Convergence Methods for Random Processes with Applications to Stochastic Systems Theory

Kushner, Harold J.

ISBN 10: 0262110903 / ISBN 13: 9780262110907

Edité par The MIT Press, 1984

A propos de cet article

*Price HAS BEEN REDUCED by 10% until Monday, July 28 (SALE ITEM)* 361 pp., Hardcover, fine in a lightly worn else very good dust jacket. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

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Détails bibliographiques

• Titre: Approximation and Weak Convergence Methods for Random Processes with Applications to Stochastic Systems Theory
• Éditeur: The MIT Press
• Date de parution: 1984
• Langue: anglais
• ISBN à 10 chiffres: 0262110903
• ISBN à 13 chiffres: 9780262110907
• Reliure: Couverture rigide
• État de l'article: Fine

A propos de ce titre

Synopsis

Control and communications engineers, physicists, and probability theorists, among others, will find this book unique. It contains a detailed development of approximation and limit theorems and methods for random processes and applies them to numerous problems of practical importance. In particular, it develops usable and broad conditions and techniques for showing that a sequence of processes converges to a Markov diffusion or jump process. This is useful when the natural physical model is quite complex, in which case a simpler approximation la diffusion process, for example) is usually made. The book simplifies and extends some important older methods and develops some powerful new ones applicable to a wide variety of limit and approximation problems. The theory of weak convergence of probability measures is introduced along with general and usable methods (for example, perturbed test function, martingale, and direct averaging) for proving tightness and weak convergence. Kushner's study begins with a systematic development of the method. It then treats dynamical system models that have state-dependent noise or nonsmooth dynamics. Perturbed Liapunov function methods are developed for stability studies of nonMarkovian problems and for the study of asymptotic distributions of non-Markovian systems. Three chapters are devoted to applications in control and communication theory (for example, phase-locked loops and adoptive filters). Smallnoise problems and an introduction to the theory of large deviations and applications conclude the book.

This book is the sixth in The MIT Press Series in Signal Processing, Optimization, and Control, edited by Alan S. Willsky.

À propos de l?auteur

Harold J. Kushner is Professor of Applied Mathematics and Engineering at Brown University and is one of the leading researchers in the area of stochastic processes concerned with analysis and synthesis in control and communications theory.