Probability and Computing: Randomized Algorithms and Probabilistic Analysis | 
 enlarge | Authors: Michael Mitzenmacher, Eli Upfal
Publisher: Cambridge University Press
Category: Book
List Price: $62.00
Buy New: $44.64
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Rating:  5 reviews
Sales Rank: 167976
Media: Hardcover
Pages: 368
Number Of Items: 1
Shipping Weight (lbs): 1.8
Dimensions (in): 10.2 x 7 x 1
ISBN: 0521835402
Dewey Decimal Number: 518.1
EAN: 9780521835404
Publication Date: January 31, 2005
Availability: Usually ships in 24 hours
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Product Description
Assuming only an elementary background in discrete mathematics, this textbook is an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It includes random sampling, expectations, Markov's and Chevyshev's inequalities, Chernoff bounds, balls and bins models, the probabilistic method, Markov chains, MCMC, martingales, entropy, and other topics. The book is designed to accompany a one- or two-semester course for graduate students in computer science and applied mathematics.
Book Description
Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communication networks and secure protocols.Assuming only an elementary background in discrete mathematics, this textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses, including random sampling, expectations, Markov's and Chevyshev's inequalities, Chernoff bounds, balls and bins models, the probabilistic method, Markov chains, MCMC, martingales, entropy, and other topics.
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| Customer Reviews:
 Advanced probability topics without measure theory August 18, 2007
Jung Dalg (Bangkok)
13 out of 14 found this review helpful
This book is underestimated by two reviewers below. I totally do not agree with them. This book covers a wide range of topics in a very readable style. The contents in this book is complementary to the book of Motwani and Raghavan (but this book is much easier to digest).
It, without requiring any knowledge on measure theory, contains excellent introductions to many difficult topics in probability including
- concentration bounds (Chernoff, Azuma-Hoeffding, etc.)
- applications of stochastic processes such as queuing theory
- martingale (Wald's equation)
- coupling of Markov chains and their mixing times
- Shannon's source coding and noisy channel theorems
- Erdos' probabilistic method
- etc.
All of these topics are provided with excellent applications in computing.
The authors illustrate many clever tricks for proving theorems, and these tricks give insights to the readers as well.
Great Book! November 9, 2007
Olumuyiwa Oluwasanmi (Albuquerque NM)
3 out of 3 found this review helpful
I have used this book over and over again. As a gentle introduction to Randomised Algorithms, the book succeeds very well. Anyone complaining about the book not explaining stuff are entitled to their opinion. This is the best introduction to Randomised Algorithms you will ever find.
Good Introductory Textbook March 15, 2005
John Matlock (Winnemucca, NV)
14 out of 43 found this review helpful
It's pretty easy to get computers to do things where the answer is yes or no, or 4 or 6, given that the inputs to the problem are known. It's much harder to get an answer to a problem where the answer is that their is a 62% chance that the answer is yes. Unfortunately, in real life it's this second class of problems that predominates.
This book is oriented to solving these kinds of real world problems. The exercises in the book are chosen from real world examples -- what we used to call story problems. This tends to give the student a better understanding of not only the mathematics and programming involved but experience in looking at problems with a view to understanding this approach to solving the problem.
This book is suitable for a one or two semester introductory class at the upper undergraduate or beginning graduate level.
Just a word about the illustration on the front of the book. At the end of the book Alice in Wonderland the queen is about to order Alice beheaded. Alice says, "You're nothing but a pack of cards." At this, the whole pack rose up into the air and came flying down around her. This illustration is by John Tenniel from the original book of 1899. A deck of flying playing cards is a good way to illustrate random and probability.
 Just unnecessary May 17, 2007
anon (USA)
13 out of 17 found this review helpful
This book, while written by two renowned computer scientists, is truly disappointing. In trying to discuss randomness and computation, this book just does a mediocre job on discussing randomized computation and also an equally poor job discussing relevant aspects of probability theory. Their approach is not novel and many of their examples can be found in other texts. If you really want to learn randomized computation, get Motwani et al's book on Randomized Algorithms. If you want to learn probability theory, get any advanced probability theory book like Spencer and Alon on the probabilistic method, one of Sheldon Ross's books, or even Grimmett and Stirzaker. Whatever you do don't get this weak hybrid of a book that will require you to get another book at some point to supplement your understanding.
 Another poorly written text book March 19, 2006
The Black Cloud (Tampa, FL)
16 out of 39 found this review helpful
The authors must be smart guys. They obviously understand alot about this subject but make the mistake that you do too! As a result, the book is inadequate as a teaching tool.
They use only half to a third of the narrative they need to adequately explain a subject. They also like to leave out proof steps or not explain them. The problems at the end of chapters are poor as well, since the authors seem to have forgotten to teach the techniques needed to solve most them in the chapter they belong to.
I am sure to them it is intuitive.
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