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(More customer reviews)The book arises from lectures at UNC-CH in a two-semester course on stochastic models. The author has exceptional precision and organization in the classroom, and this comes through in the book. Great book for teachingfrom, as well as learning from. This book also takes a step which more venerable texts, such as the Trivedi book or the Ross series could not -- it has computational excercises suitable for math packages like Mathematica. Thus, the student can be introduced to scientific computer literacy as well as stochastic processes. I recommend this book to anyone interested in teaching today's student, or for preparing themselves for challenges in Operations Research. The book would make an above-average reference as well. Mike Bailey Associate Professor, Naval Postgraduate School
Click Here to see more reviews about: Modeling and Analysis of Stochastic Systems, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
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 EditionA new chapter on diffusion processes that gives an accessible and non-measure-theoretic treatment with applications to financeA more streamlined, application-oriented approach to renewal, regenerative, and Markov regenerative processesTwo appendices that collect relevant results from analysis and differential and difference equationsRather 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-based programscan be downloaded from the author's websiteand a solutions manual is available for qualifying instructors.
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