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The fourth edition of "Probability, Random Variables and Stochastic Processes" has been updated significantly from the previous edition, and it now includes co-author S. Unnikrishna Pillai of Polytechnic University. The book is intended for a senior/graduate level course in probability and is aimed at students in electrical engineering, math, and physics departments. The authors' approach is to develop the subject of probability theory and stochastic processes as a deductive discipline and to illustrate the theory with basic applications of engineering interest. Approximately 1/3 of the text is new material - this material maintains the style and spirit of previous editions. In order to bridge the gap between concepts and applications, a number of additional examples have been added for further clarity, as well as several new topics.
- Sales Rank: #714037 in Books
- Brand: Brand: McGraw-Hill Europe
- Published on: 2002-01-01
- Format: International Edition
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x 1.26" w x 7.32" l, .0 pounds
- Binding: Paperback
- 852 pages
- Used Book in Good Condition
Amazon.com Review
This text is a classic in probability, statistics, and estimation and in the application of these fields to modern engineering problems. Probability, Random Variables, and Stochastic Processes assumes a strong college mathematics background. The first half of the text develops the basic machinery of probability and statistics from first principles while the second half develops applications of the basic theory. Topics in the first section include probability distributions and densities, random variables and vectors, expectations, covariance, correlations, functions of random variables and vectors, and conditional distributions and densities. In this third edition of the text, the second half of the book has been substantially updated and expanded to include new or revised discussions of the following topics: mean square estimation, likelihood tests, maximum entropy methods, Monte Carlo techniques, spectral representations and estimation, sampling theory, bispectra and system identification, cyclostationary processes, deterministic signals in noise, and the Wiener and Kalman filters. Probability, Random Variables, and Stochastic Processes covers a remarkable density of material and the clarity of both presentation and notation make this book invaluable as a text and a reference.
About the Author
S. Unnikrishna Pillai is a Professor of Electrical and Computer Engineering at Polytechnic Institute of NYU in Brooklyn, New York. His research interests include radar signal processing, blind identification, spectrum estimation, data recovery and wavform diversity. Dr. Pillai is the author of Array Signal Processign and co-author of Spectrum Estimation and system Identification, Prof. Papoulis' Probability, Random Variables and Stochastic processes (Fourth edition), and Space Based Radar - Theory & Applications.
Most helpful customer reviews
52 of 53 people found the following review helpful.
Found no substitute for a difficult subject
By N N Taleb
When readers and students ask to me for a useable book for nonmathematicians to get into probability (or a probabilistic approach to statistics), before embarking into deeper problems, I suggest this book by the Late A. Papoulis. I even recommend it to mathematicians as their training often tends to make them spend too much time on limit theorems and very little on the actual "plumbing".
The treatment has no measure theory, cuts to the chase, and can be used as a desk reference. If you want measure theory, go spend some time reading Billingsley. A deep understanding of measure theory is not necessary for scientific and engineering applications; it is not necessary for those who do not want to work on theorems and technical proofs.
I've notice a few complaints in the comments section by people who felt frustrated by the treatment: do not pay attention to them. Ignore them. It the subject itself that is difficult, not this book. The book, in fact, is admirable and comprehensive given the current state of the art.
I am using this book as a benchmark while writing my own, but more advanced, textbook (on errors in use of statistical models). Anything derived and presented in Papoulis, I can skip. And when students ask me what they need as pre-requisite to attend my class or read my book, my answer is: Papoulis if you are a scientist, Varadhan if you are more abstract.
36 of 39 people found the following review helpful.
Good book with the help of a good instructor
By JS
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.
17 of 23 people found the following review helpful.
Horrible is being too kind
By T. A. Brown
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.
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