Couverture rigide

ISBN 10 : 1439808759 ISBN 13 : 9781439808757

Editeur : Chapman and Hall/CRC, 2009

Based on the author's more than 25 years of teaching experience, Modeling and Analysis of Stochastic Systems, Second Edition covers the most important classes of stochastic processes used in the modeling of diverse systems, from supply chains and inventory systems to genetics and biological systems. For each class of stochastic process, the text includes its definition, characterization, applications, transient and limiting behavior, first passage times, and cost/reward models. Along with reorganizing the material, this edition revises and adds new exercises and examples. New to the second edition: a new chapter on diffusion processes that gives an accessible and non-measure-theoretic treatment with applications to finance; a more streamlined, application-oriented approach to renewal, regenerative, and Markov regenerative processes; and, two appendices that collect relevant results from analysis and differential and difference equations. Rather than offer special tricks that work in specific problems, this book provides thorough coverage of general tools that enable the solution and analysis of stochastic models. After mastering the material in the text, students will be well-equipped to build and analyze useful stochastic models for various situations. A collection of MATLAB[registered]-based programs can be downloaded from the author's website and a solutions manual is available for qualifying instructors.

About the Author :

Vidyadhar G. Kulkarni is a Norman Johnson Professor in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill.

Review :

This book is written by a well-experienced scientist and teacher. The material is carefully chosen and presented in a systematic way, starting with the basics and reaching more advanced topics. ? It is very useful to see a large number of examples worked out in detail. Thus, besides university courses, the book can be successfully used for self-education. ? This book is a good example of how to write on sophisticated topics about stochastic processes without involving the heavy measure theoretic machinery. Another advantage of the book is the application-oriented approach that is followed by the author. ? This book can be strongly recommended for introductory, intermediate or advanced university courses on stochastic processes. Both students and their teachers may benefit considerably from it.
?Journal of the Royal Statistical Society: Series A, July 2011

? an accessible, well paced, and very nicely presented book. The publishers are also to be commended on its nice production: