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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: #1370967 in Books
- Brand: Brand: McGraw-Hill Science/Engineering/Math
- Published on: 2001-12-14
- Original language: English
- Number of items: 1
- Dimensions: 9.50" h x 1.40" w x 7.50" l,
- Binding: Hardcover
- 304 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
61 of 62 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.
6 of 6 people found the following review helpful.
Classic Book on a Very Difficult Topic
By sp
I cut my teeth from the 2nd edition of this classic text and later actually went out and bought the 1st edition (1965)
because multiple people that I work with mentioned that later editions were watered down compared to the original edition.
I think a more accurate statement is that more applications chapters were added in later
editions (entropy, queuing theory, etc..) and the first edition is geared more toward laying out the basic underlying theory.
In any case, any engineer or student working in Kalman filtering or communications would be well served by
having a copy of this book at his/her reach. In my opinion there is never any one best book on any topic but this
book is an element of the spanning set of books that should be consulted by engineering students/professionals on this difficult topic. Other classic books that I would
recommend along with Papoulis are
1. Probability and Stochastic Processes for Engineers by Helstrom (written by one of the fathers of modern detection theory)
2. An Introduction to Probability and Stochastic Processes by Melsa and Sage (Dover has recently reprinted this classic)
Although I am not a big fan of newer textbooks the following books are the best of the more recent texts
1. Ibe, "Fundamentals of Applied Probability and Random Processes" (this book is very straightforward and written for the average student; good place to start for the novice)
2. Kay, "Intuitive Probability and Random Processes using MATLAB" (excellent book; best of all modern texts)
3. Dolecek, Random Signals and Processes Primer with MATALB (really brings the subject to life...best used as supplementary reading)
4. Jacobs, "Stochastic Processes for Physicists" (learn the Ito calculus painlessly... Book is also a good intro for engineers despite the title)
4 of 4 people found the following review helpful.
Material Presented in Clear Albeit Unorganized Fashion
By Daniel Greenheck
I'm using this book for a a graduate level engineering course on probability theory and random stochastic processes. I took a probability theory course in undergrad and ended up getting a C, so I was very worried about approaching the subject a second time. There's no getting around the fact that the subject area is difficult. However, this book explains the concepts quite well and provides ample examples.
However, the examples also get in the way of the core material. The book is laid out very poorly and there is little organization within the chapters themselves. The delineation between the theory and the examples is not easily apparent, so it often feels like a jumbled mess. Honestly, a better layout with the examples perhaps separated from the core material and I would rate this book 5-stars.
In conclusion, I recommend this book, but the lack of organization means self-study is more of a chore than it should be.
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