Customer Reviews

An Introduction to Stochastic Modeling, Third Edition

5 star:		(3)
4 star:		(2)
3 star:		(1)
2 star:		(3)
1 star:		(0)

 

 

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.

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.

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.

This book opens with a nose dive into Conditional probability. Unlike some other authors that devote a half their entire bloody book on review of probability, random variables, and conditional probability distributions, this book assumes a firm or atleast an introductory knowledge of the above. Ideally, a good probability book such as Hogg and Tanis would prove to be quite helpful as a supplementary reference.

Markov Chains and Processes are introduced in the third chapter and the definition is lucid, complete with examples that are easy to comprehend. One of the examples that calculated the frequency with which an autoparts store must replenish its stock was absolutely brilliant and made things a lot easier to understand. The exercises are rather thorough, so if you are purchasing this book for a class and will be assigned homework assignments from the text, be prepared to devote atleast an hour on an average to each problem.

The book is relatively easy to read, if you have a good background in random variables, and hence, i repeat, keep a book on introductory probability and statistics handy.

First, let me say that I found the content of this book to be, on the overall, wonderful and fairly well explained. Concepts are presented well and, unlike many other books on Stochastic Modeling, sigma algebra is avoided (this is a definant plus for making it into an undergrad or low-level grad textbook).

That having been said, this book has some of the worst organization I have ever seen in a textbook. Every chapter is divided into sections and at the end of each section there are questions which are separated into "Exercises" and "Problems"; this in-and-of itself is not as much of a problem as that everything is numbered the same way.

Therefore problem 5 in section 4 chapter 3 is numbered the same way (4.5) as exercise 5 in the same section and chapter is numbered the same way as exercise/problem 5 in the same section of any other chapter in the book. The only real difference between "Exercises" and "Problems" is that exercises tend to be answered in the back of the book.

There are also other organizational difficulties in the text itself--such as that it is never entirely clear where the examples are in the text: there are several things which are labeled as examples (and are), however, over half of the examples in some chapters seem to be simply thrown into the text without any special indicator that they are examples of what is being discussed.

While the content in this book is good, the organization is so wretched that I have to knock it down two stars.

I used this book for an applied stochastic introductory course. Though this book is application oriented, and though the book starts from the basics, this book is accessible to those who have already done an introductory course on applied probability (at least must have knowledge of distributions, central limit theorem). The book is well written and is easy to follow. The exercises and problems are really elucidating. The chapter on Markov Chains is as good as it gets. The one negative point I can think of is that, this book does not cover Hamilton Jacobi Bellman Equation. However if you want an introduction to applied stochastic, this would be the first book I would recommend.

Letting the other reviews critique the content, I would like to mention that I was unimpressed with the quality of the binding.

Although I may be a unique occurence, the binding of my book was cheap and broke so that several pages came loose. For an $80 book I expect better durability than a paperback.

I think it's a very good book about Stochastic modeling; with comprehensive examples. It's a good book for introductory courses on this field.

I took a course in Stochastic Processes using this book... this book attempts to review some of the statistics that you would need to understand the major topics of this course; however, your background in stats MUST be bulletproof else you will be quite lost in the woods. It helps if you have a very good instructor teaching the course without having to figure out the difference between his symbols and the authors' symbols! (NOTE: I took this course as a graduate student at Wayne State in Detroit... this course can be a career killer; I ended up dropping the course halfway into the semester!)