i did not like this book. not well organized, missing details here and there, and typos. all these make it hard to follow.

When I first used this book it was my first intro to probability ever. The text was very hard to read and understand and I had to go to the book by Yates/Goodman to get grounded in the basics. However, after some years I came back to it for review and this book is actually really good. It covers a lot of aspects of statistics, signal processing, pure mathematical probability theory, not just engineering-applied probability theory (and random processes of course). Even some stochastic calculus is thrown in. Of course there are other good books on the subject that range in focus, but this one is very good for (especially) engineers studying the subject at the graduate level and beyond. It is especially good for signal processing.

I am amazed that this book is used as a primary text at so many universities. While initially I was unsure if this was a poor text, or if I was simply not adequately prepared to take a probability course, I qucikly realized upon viewing other references that this book was useless and that there are so many other books on this subject which address probability in an intuitive manner. I sometimes read this book and wonder who the authors were writing this book for. The examples are completely overbearing in some cases. Take Example 5.5-3, which was a 4 page discourse on Black Lung, which was probably pulled out of a journal paper. While this could be an interesting topic in probability, it is frustrating, confusing and discouraging to someone trying to learn the subject. I will not even go into the books typographical errors, as they have been mentioned in several other reviews. My advice would be to check out other references if you are taking a class using this text. This book is useful only to people who have mastered the subject.

This book is ideal as a textbook in a class on random processes, particularly for engineers and those interested in signal processing and telecommunications. I have found the book very easy to follow, quite accessible and complete, and the example problems are very indicative of the approach you need to solve the exercises at the end of each chapter. I would not recommend this book for self-study, however, as I think that self-study of a subject as difficult as random processes would be tough going for anybody. The criticisms that I would make are:
1. The book is poorly edited. There are a moderate number of typos. Some are in places where it is obvious what the author meant, but a few are in critical equations that could mislead the reader.
2. There are no solutions to any of the exercises included in the book. It would really help if there were solutions to either odd or even problems included so that you would know you are on the right path.
Since Amazon does not currently show the table of contents for this book, I do so for the purpose of completeness:
Chapter one is an introduction to probability. This material is covered quickly, so the reader should just use this as a review.
Chapter two introduces random variables. Included topics are the definition of a random variable, the probability density and distribution functions. This material is presented with exceptional clarity.
Functions of Random Variables are introduced in chapter three. This is one of the hardest chapters in the book, although I have not been able to find another book that explains the same material as well. This is mainly concerned with finding the probability density functions of f(x) and f(x,y) given the pdf of the input functions. Convolution and multiple integrals abound in this chapter.
Chapter 4 is Expectation and Introduction to Estimation. This sounds straightforward, but the material on conditional expectation can get complex, although the book covers it well.
Chapter 5 is Random Vectors and Parameter Estimation. This chapter takes concepts from numerical linear algebra and applies it to random processes.
Chapter six is random sequences and introduces linear systems concepts and markov processes.
Chapters seven and eight talk about advanced concepts in random processes.
Chapter nine discusses applications of the previous eight chapters to statistical signal processing.
To get the most out of this book you should already be familiar with probability theory, multiple variable calculus, and linear algebra. If you are not, there is no way you are going to understand this material. A good companion to this book is Schaum's outline of Probability, Random Variables, and Random Processes. It covers most of the same material as this book, except that it does so with more of a mathematician's viewpoint. The Schaum's outline's solved problems also help offset the fact that there are no solutions to exercises in this book. Just remember that there is no textbook on a subject as complex as random processes that is going to negate the need for an instructor of exceptional ability.

This book is well written, and is especially interesting for electrical engineers because it uses examples from their field almost from the start. However, it is rife with typos, which can be frustrating in a math book, where you often assume its true, then try to figure out why. Also, it exhibits the sloppy math style common to engineers, especially when it comes to the distinction between constants and variables. This can lead to alot confusion at first.

I found Probability and Random Processes a difficult subject and it was compounded by the instructor's selection of the stark and woods textbook. While it has some interesting problem sets and the tone of the text is very engaging, Probability and Random Processes, 3rd ed. has several problems. First of all, the book has several typos, including some in derivations which can cause a "what the...?" moment. Second, many sections in the introductory material gloss over derivations, skip steps, or leave proofs of theorems as the classic "exercize for the reader." (!) The biggest problem the text has is that it doesn't adequately explain the intuition for problem solving. For an engineer coming from industry and starting grad school after an extended period away from college, the net result is that the book is not self-contained. I had to buy the Papoulis book and the Schaum's outline to keep my head above water. When the semester was over I ditched the Stark and Woods and kept the Papoulis book.