Introduction to Modeling and Analysis of Stochastic Systems

This is an introductory-level text on stochastic modeling. It is suited for undergraduate students in engineering, operations research, statistics, mathematics, actuarial science, business management, computer science, and public policy. It employs a large number of examples to teach the students to use stochastic models of real-life systems to predict their performance and use this analysis to design better systems.

The book is devoted to the study of important classes of stochastic processes: discrete and continuous time Markov processes, Poisson processes, renewal and regenerative processes, semi-Markov processes, queueing models, and diffusion processes. The book systematically studies the short-term and the long-term behavior, cost/reward models, and first passage times. All the material is illustrated with many examples and case studies. The book provides a concise review of probability in the appendix.

The book emphasizes numerical answers to the problems. A collection of MATLAB programs to accompany the this book can be downloaded from unc.edu/