26 of 27 people found the following review helpful:
4.0 out of 5 stars
good applied stochastic processes book, February 24, 2008
This review is from: An Introduction to Stochastic Modeling, Third Edition (Hardcover)
Since the third edition is out and I have not seen I must say upfront that my comments are on an earlier edition. This pair of authors have done an excellent job with three textbooks on stochastic processes. Unlike the other two books that give a rigorous treatment of stochastic processes this book is more applied with the emphasis on examples and models for real applied problems. Introductory probability and statistics is assumed by the authors and they deal with all the standard topics.
However, I do have to agree with the other reviewers who criticize the organization. Karlin and Taylor are not known for great organizational skills. Sometimes that is a trait of a mathematical genius. Sam Karlin can certainly be put into that category. Although I never took a course from him at Stanford, the fact that he was a Stanford professor meant that I did learn a lot about him and see him on campus and hear an occasional lecture. Of course this was a long time ago in the mid 1970s when the first edition of the first course was out and the other two books had probably not yet been contemplated.
In any case this would be a good reference for an applied statistician to have.
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11 of 11 people found the following review helpful:
2.0 out of 5 stars
So Much Potential, October 18, 2005
This review is from: An Introduction to Stochastic Modeling, Third Edition (Hardcover)
I purchased this book to use as the text for a graduate level Stochastic Processes course that I am taking by independent study, and have had a large role in designing. I purchased the book, sight unseen, based on reviews that indicated there were many examples with solutions, wary that reviews also mentioned a lack of organization.
The organization was worse than I could have anticipated, and is one of two major flaws that do not render the book unusable, but make it very unpleasant to work with.
As has been mentioned, the outline numbering system makes chapters harder to follow, rather than easier, and it is difficult to distinguish the exercises with solutions from the problems with no solutions. This strange numbering system is carried out in the answer key portion, as well. When I read similar comments in reviews, I thought, how bad can it be? Creatively bad.
The most problematic organizational point, however, is the fact that concepts are covered in homework problems before they are introduced in the text. Chapter 1, for example, contains problems that could only be done after reading Chapter 2. This juxtaposition of discussion and exercise is still taking place as I am about three fourths of the way through the book.
The second issue with this text, besides the confusing organization, is the cumbersome use of notation with no key or explanation. Commonly, sections of text are only three or four pages long. They consist of, "Here is a formula. Now here is the proof," without any real explanation of what the formula is for, and perhaps, worse, no indication of what the variables stand for. The field of statistics is notorious for it's inconsistent use of symbology. Most texts address this by including a key of symbols. Not only has a key not been included in this text, but the symbology is most uncommon. It has taken me quite a bit of searching to decipher a number of symbols for which there were much more common alternatives.
In it's favor, the exercises and problems in the book are good, appropriate, and even classic examples.
With a strong enough background in probability, particularly Markov processes, or, with good instruction, this book is a decent source of exercises. But certainly there are better sources of exercises if we must look elsewhere for instruction.
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9 of 9 people found the following review helpful:
4.0 out of 5 stars
A very good introductory book, February 9, 2000
This review is from: An Introduction to Stochastic Modeling, Third Edition (Hardcover)
The book shows through examples the very vast collection of stochastic models without going too deep in the technical details. I consider the book a good introduction for undergraduate students with a calculus and probability course. Most adequately for engineers than mathematicians.
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