12/03/2011

Modeling Random Processes for Engineers and Managers Review

Modeling Random Processes for Engineers and Managers
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Author James Solberg claims that his book has a non standard presentation for probability and random processes. He is absolutely right! For example, he devotes a whole chapter (Chapter 2) to modeling Markov chains, before doing any of the usual calculation methods (chapter 3).
In other books, a small amount of such material would appear as examples or exercises. But Solberg deems it worthy of a whole chapter, and he specifically states properties that would normally be overlooked. For example, he talks about converting count data into transition matrices, he talks about different possible ways of defining step size, he introduces lumpability to reduce the size of the state space.
The book is full of examples,chosen to illustrate practical uses of the material, or selected to add understanding. This book is student friendly. I lent my copy to a graduate student and after returning the book, he commented that the book greatly improved his understanding of stochastic processes. The book is at a lower level than many other books on the subject, but not much lower. The author's years of experience enable him to recognize where a student would normally have difficulties and the author takes steps to add explantion to enhance understanding. Chapter 8 gives a new path counting method to find limiting probabilities for Markov chains and continuous time Markov processes. Chapters 6 and 7 deal with queueing and queueing networks. The book is consistently well written. I think that this is a splendid book for a nonprobabilist to become introduced to the material. I have never met James Solberg, but the queue on the front cover includes a cheerful man who seems older than the others. I hope that man is Solberg. Even if not, the smile on the man's face comes through in the writing of the book.

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By reducing mathematical detail and focusing on real-world applications, this book provides engineers with an easy-to-understand overview of stochastic modeling. An entire chapter is included on how to set up the problem, and then another complete chapter presents examples of applications before doing any math. A previously unpublished computational method for solving equations related to Markov processes is added. The book shows how to add costs or revenues to the basic probability structures without much additional effort. In addition, numerous examples are included that show how the theory can be used. Engineers will also find explanations on how to formulate word problems into the models that the math worked on.

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