Introduction to Modeling and Analysis of Stochastic Systems [Paperback]

This book provides a self-contained review of all the relevant topics in probability theory. A software package called MAXIM, which runs on MATLAB, is made available for downloading. Vidyadhar G. Kulkarni is Professor of Operations Research at the University of North Carolina at Chapel Hill.

Introduction.- Discrete-Time Markov Models.- Poisson Processes.- Continuous-Time Markov Models.- Generalized Markov Models.- Queueing Models.- Brownian Motion.

From the reviews of the second edition:

The author has added a new chapter on Poisson processes and another one on Brownian motion. The discussion is kept on an elementary level and does not require any knowledge from measure theory or advanced calculus. & the text is suitable for an undergraduate course on probabilistic modeling for students from physics, engineering, operations research, computer science, business administration or some related field that needs advanced modeling techniques. (H. M. Mai, Zentralblatt MATH, Vol. 1222, 2011)

Suitable for undergraduates in Mathematics, Statistics, Operations Research, Computer Science, Business Administration, Public Policy, etc. This is a very clear and readable text on Markov chains, Poisson processes, continuous time Markov chains, renewal processes, and queuing processes. & The treatment is very clear, intuitive as well as rigorous, without being pedantic, and full of interesting examples and case studies. & The book should be fun to teach from and learn from. (Jayanta K. Ghosh, International Statistical Review, Vol. 80 (3), 2012)

V. G. Kulkarni is Professor in the Department of Statistics and Operations Research in the University of North Carolina, Chapel Hill. He has authored a graduate-level text Modeling and Analysis of Stochastic Systems and dozens of articles on stochastic models of queues, computer and communications systems, and production and supply chain systems. He holds a patent on tral“M