An Introduction to Stochastic Modeling

Rating:
Summary: Good book overall, organization is very poor
Review: 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.

Rating:
Summary: Good book overall, organization is very poor
Review: 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.

Rating:
Summary: A little too much waffle
Review: Good points: A couple of good review chapters on basic probability theory (good for reference), lots of worked examples and exercises classified by level of difficulty, from the pissy-easy to the very challenging.

Bad points: The notation is strange at times. Very often, the treatment of limits is neither rigorous nor intuitively helpful, and a few things are repeated over and over (the axioms of a Poisson process, for example). In my view, a good paragraph of text is better than two pages (good or bad), and clarity and conciseness do not seem to be the authors' fortes. I'm sure this book would be in pretty good shape if it just lost some weight.

I was very surprised by not being able to find the law of large numbers written in a precise mathematical formula anywhere in the book, especially when its importance is stated in the introduction.

The material is not very nicely organized. This is the "chapter 3, section 4, subsection 2, subsubsection 6" type of book.

Having pointed out its defects, I have to say that I found this book to be a good and interesting introduction to stochastic processes. It's also one of the most "introductory" I've seen (the reader who complains about the level should know that, in most universities, an upper-division probability course is a prerequisite for a stochastic processes course).

Feedback for Academic Press: the format is not very attractive; even with all the waffle, that book could be half as thick. (Take example from Ross's "Stochastic Processes" or Rudin's "Principles of Mathematical Analysis.")

Rating:
Summary: Taylor dares call this an introduction...
Review: 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!)

Rating:
Summary: A very good introductory book
Review: 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.