Modeling and Analysis of Stochastic Systems
Building on the author’s more than 35 years of teaching experience, Modeling and Analysis of Stochastic Systems, Third Edition, covers the most important classes of stochastic processes used in the modeling of diverse 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.
The third edition has been updated with several new applications, including the Google search algorithm in discrete time Markov chains, several examples from health care and finance in continuous time Markov chains, and square root staffing rule in Queuing models. More than 50 new exercises have been added to enhance its use as a course text or for self-study. The sequence of chapters and exercises has been maintained between editions, to enable those now teaching from the second edition to use the third edition.
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, readers will be well-equipped to build and analyze useful stochastic models for real-life situations.
"The third edition of Modeling and Analysis of Stochastic Systems remains an excellent book for a graduate-level study of stochastic processes. The aim of the book is modeling with stochastic elements in practical settings and analysis of the resulting stochastic model. The target audience is quantitative disciplines such as operations research, statistics, computer science, economics, and others where the book is well positioned, since it is application-driven and does not require measure theoretic probability. … The numerous exercises, separated into modeling, computational, and conceptual classes, are a strength of this text. The author also notes that most come from his own homework and exams, making them a valuable resource."
—James M. Flegal, University of California, Riverside, in Journal of the American Statistical Association, January 2018