Rating: 
Summary: Good book with the help of a good instructor
Review: After reading almost all reviews, I actually think I agree with almost all including the ones that say it is not a book for beginners and ones that say that it is a great book written by a very knowledgeable author. As a student who just completed a course in this subject with this book as the text, here is what I would like to say:
1. My instructor re-ordered some of the content of the text for his course. It seemed necessary to understand certain concepts.
2. As one of the previous reviews mentioned, some of the important aspects of probability theory were hidden in problems, these were brought out by the instructor either as homeworks or as part of lecture notes and explained. This also means that it is not enough to just read this text and understand the examples. It is almost as important to go through the problems to get the complete picture
3. All in all I liked the book but I sure would not have liked it as much if I did not have a good instructor to go with it. Definitely not a self-study book.
Rating: 
Summary: Save your money
Review: Get another book, Stark and Woods is quite good, not an excellent introduction but still much better. Papoulis is viewed on by our lecturers as being close to the THE book on the subject - probably due to the fact that they used it in their time. AS long as you understand the concept of Random Variables as a transformation mapping, Stark & Woods will take you by the hand and show you the landscape of the immensely important subject. It is quite example driven but does seem considerably better that Papoulis(maybe better as a reference rather than a text book)
Rating:
Summary: Can't get any worse
Review: I encountered this book in my first year of graduate school in Electrical Engineering. I found the book utterly useless. I was a very good student all through my schooling and yet I could not read and learn a single thing from this book and I was not the only one. I think the author does not even try to explain the subject in a way that is remotely comprehensible to the novice, on the contrary, he tries the impress the reader by using complicating expressions and long wording and to prove his superiority. There are countless number of books on the subject that one can use and even enjoy and this is not certainly one of them. The book is boring and dry. I would recommend this book ONLY as a reference. Even then it has sever short comings.
Rating:
Summary: Can't get any worse
Review: I encountered this book in my first year of graduate school in Electrical Engineering. I found the book utterly useless. I was a very good student all through my schooling and yet I could not read and learn a single thing from this book and I was not the only one. I think the author does not even try to explain the subject in a way that is remotely comprehensible to the novice, on the contrary, he tries the impress the reader by using complicating expressions and long wording and to prove his superiority. There are countless number of books on the subject that one can use and even enjoy and this is not certainly one of them. The book is boring and dry. I would recommend this book ONLY as a reference. Even then it has sever short comings.
Rating: 
Summary: Papoulis is Useful
Review: I first encountered the works of Papoulis when just out of graduate school in pure math, and worked for a major defense contractor as an analyst. I found out that almost all the engineers there had this book, and purchased a copy. I had studied stochastic processes at a much more theoretical level than is presented in this book, and that study was significantly more difficult than the material in the text under review, so complainers take note. Why do I think this book an excellent one? Because it is so eminently USEFUL to the working engineer. I believe that has been the intent of the author in all of his works. If you're a working engineer who needs to find answers to tough problems, you can scarcely do better than to consult Papoulis.
For example, the material on power spectra is of more than academic interest and is useful in applications; the bivariate Taylor expansion for moments of a function of two distributions has been used again and again in applications in industry; especially in the analysis of the ratio of noisy variables arising from radar measurements. The point is that the text provides the material in a readily accessible way for someone who needs it in the "real world" of engineering analysis.
Rating:
Summary: One of the most readable books on the subject
Review: I first encountered this book in a first, graduate-level, course on probability and random processes. Although I was by far not the best student of the subject, and never went into comm theory professionally, this book kept me afloat. I found it to be one of the most readable books on a difficult subject that I have ever encountered. The author does a fine job of focussing on the important ideas, developing them completely, and presenting them clearly. I'm really surprised by the negative reviews of this book. If these complaints come from undergraduates, I can sympathize a bit. This is definitely a graduate-level book, and I think it is a mistake to use in in an undergrad course. On the other hand, I can't sympathize with such complaints if they from grad students. The complaint seems to be that it doesn't make the subject "easy"; unfortunately, there is nothing "easy" about stochastic processes. Some things in life require effort and commitment.
Rating:
Summary: Horrible is being too kind
Review: I was forced to use this book for a course during my first year in Graduate School for Computer Engineering. I must say that this book is among the worst, if not the worst, I've encounterd. Concepts are never explained with any great degree of clarity: the authors' writing style is convoluted almost to the point of being indecipherable, new concepts are introduced with only minimal effort given to explaining their foundation and usefulness, and, conversely, concepts are proved using formulas/theorems that haven't yet been presented. If I hadn't previously taken a course using the excellent book "Probability and Statistics for Engineering and the Sciences" by Jay Devore, I wouldv'e been at a complete loss trying to understand the topics in this book.
If there is no way that you can avoid using this book, and if acquiring a well-written, logically-organized companion textbook like the one I mentioned above isn't feasible, then at a minimum I recommend buying "Schaum's Outline of Probability, Random Variables, and Random Processes" -- it at least clearly presents the concepts. By the way, the price of this book is completely unjustifiable -- it's blatant robbery.
Rating: 
Summary: great book in the classical textbook sense
Review: i would like to again point out that while this book is excellent as a reference, it's not very good to read cover-to-cover. Papoulis is extremely knowledgeable in this area, and this book is, in fact, the FIRST EVER book of stochastic/random variables and processes written for the engineers. unfortunately, despite the excellent material, the presentation and coverage is sometimes hard to follow, and hides the true gem in this book.
Rating: 
Summary: Classics are not always great.
Review: I've been told by many professors that this is *the* classic text on the topic. You might think this would make it *the* book to read on the topic, but it is definately *not* a classic introductory textbook. Everyone in my class complained about it. I found particularly frustrating it's hiding of important theorems, identities, etc. in examples rather than in the main flow of the text.
Rating:
Summary: Not a good textbook
Review: IMO, this is not a good textbook. On one hand, it never explains the purpose of the materials. I know it elaborates on the random variables and different distributions and a lot of materials in detail, but I don't know where can I use these things. On the other hand, it omits the mathematical details, too. So when I read this book, I found unclear points everywhere. Someone else recommended this book as a good engineer reference. I think that might be true if there were less errors. I find errors in the equations every two or three pages. Engineers may not need to know the details, and they know what they need to model their designs. But they need the "correct" thing to do that. Maybe that is not the author's fault but McGraw-Hill's, but to me, a reader of the textbook, it is the same. No recommendation of this book.
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