Stochastic Differential Inclusions and Applications

Stochastic Differential Inclusions and Applications
Author :
Publisher : Springer Science & Business Media
Total Pages : 295
Release :
ISBN-10 : 9781461467564
ISBN-13 : 146146756X
Rating : 4/5 (64 Downloads)

Book Synopsis Stochastic Differential Inclusions and Applications by : Michał Kisielewicz

Download or read book Stochastic Differential Inclusions and Applications written by Michał Kisielewicz and published by Springer Science & Business Media. This book was released on 2013-06-12 with total page 295 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​This book aims to further develop the theory of stochastic functional inclusions and their applications for describing the solutions of the initial and boundary value problems for partial differential inclusions. The self-contained volume is designed to introduce the reader in a systematic fashion, to new methods of the stochastic optimal control theory from the very beginning. The exposition contains detailed proofs and uses new and original methods to characterize the properties of stochastic functional inclusions that, up to the present time, have only been published recently by the author. The work is divided into seven chapters, with the first two acting as an introduction, containing selected material dealing with point- and set-valued stochastic processes, and the final two devoted to applications and optimal control problems. The book presents recent and pressing issues in stochastic processes, control, differential games, optimization and their application in finance, manufacturing, queueing networks, and climate control. Written by an award-winning author in the field of stochastic differential inclusions and their application to control theory, This book is intended for students and researchers in mathematics and applications; particularly those studying optimal control theory. It is also highly relevant for students of economics and engineering. The book can also be used as a reference on stochastic differential inclusions. Knowledge of select topics in analysis and probability theory are required.


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