Stochastic Processes Review

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(More customer reviews)I have found this book to be a fantastic resource for practitioners (including myself and colleagues) who need to find known results and/or develop new ones by applying clear probabilistic (especially, sample-path) thinking to new problems. To learn the material and frameworks for the first time, it is perhaps still impossible to beat Feller. (Grimmett and Stirzaker is a great introduction in the same tradition and friendlier than Ross.) But once one has been out in the real world doing applied stochastic modeling fro a while, this book of Ross's becomes extremely valuable! It has many results in it that are difficult or impossible to find elsewhere and that are pretty easy to locate here. Moreover, the proofs are explained very clearly, though briefly, so that if it has been a while since you have had to do problems sets, the style of thinking will come back in a hurry on reading Ross's exposition. Finally, in contrast to some other good books (like Hoel et al.), Ross's notation tends to be very clear and intuitive -- very close to what many authors choose in their journal articles -- so that one can immediately follow a particular result and exposition without having to read through a lot of the rest of the book to understand the notation. This is a real benefit for folks who just want to find and use what is known to help solve some new problems.
In summary, I agree with others that this may not be the right book to learn stochastic processes from for the first time, but it is well worth the (huge) price when you need an up-to-date, clearly explained source to help you solve real-world problems.

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Markov Processes for Stochastic Modeling (Stochastic Modeling Series) Review

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(More customer reviews)It is the worst book about stochastic processes I've ever read, it's confusing, if there were concepts clear in your mind after reading the book there won't be anymore. It is a very useful book to burn.

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Modeling Random Processes for Engineers and Managers Review

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(More customer reviews)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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