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.
FREE Two-Day Shipping for Students. Learn more |
 Sell Back Your Copy for $28.85
Whether you buy it used on Amazon for $46.00 or somewhere else, you can sell it back through our Book Trade-In Program at the current price of $28.85.
Used Price$46.00
Trade-in Price$28.85
Price after
Trade-in$17.15 |
Book Description
Special Offers and Product Promotions
- Buy $50 in qualifying physical textbooks, get $5 in Amazon MP3 Credit. Here's how (restrictions apply)
Frequently Bought Together
Editorial Reviews
Review
"An excellent book which sets off straight away in Chapter 1 with interesting motivational examples, striking the right balance between theory and application. Having both breadth and depth it is accessible and interesting to both undergraduate and graduate students. It takes the reader all the way from introductory to advanced topics and leaves them empowered with the tools to continue research on their own...It's obviously written by people who understand the subject inside out and how to explain it to students. Buy it, read it enjoy it; profit from it. It feels as if it has been well tested out on students and will work straight away."
Colin Cooper, King's College, University of London
"An exciting new book on randomized algorithms. It nicely covers all the basics, and also has some interesting modern applications for the more advanced student."
Alan Frieze, Carnegie-Mellon University
"This text provides a solid background in probabilistic techniques, illustrating each with well-chosen examples. The explanations are clear, and convey the intuition behind the results and techniques, yet the coverage is rigorous. An excellent advanced undergraduate text."
Peter Bartlett, University of California, Berkeley
"Probability is part of the conceptual core of modern computer science. Probabilistic analysis of algorithms, randomized algorithms and probabilistic combinatorial constructions have become fundamental tools for computer science and applied mathematics. This book provides a thorough grounding in discrete probability and its applications in computing,at a level accessible to advanced undergraduates in the computational, mathematical and engineering sciences."
Richard M. Karp, University of California, Berkeley
"This text presents a clear exposition of the tools of probabilistic analysis from the very basics to more advanced topics. In addition, each chapter offers a well-chosen set of problems for a range of abilities. This book is suitable for upper division undergraduates and first year graduate students in computer science and related disciplines. It will also be useful as a reference for researchers who would like to incorporate these tools into their work. I enjoyed teaching from the book and highly recommend it."
Valerie King, University of Victoria
"The structure and pace of this book are well matched to the intended audience. The authors consistently maintain a good balance between theory and applications...Good students will be challenged and yet average students will find the text very readable. This is a very attractive textbook."
MAA, Bill Satzer
"The book can be used for self-study since there are exercises in each chapter."
Mathematics of Computation
"Because of the widespread interest in the subject, a textbook covering randomization in computer science must...be many things to many different people: it should serve as an introduction to the area for an undergraduate or graduate student interested in randomized algorithms; a survey of applications and techniques for the general computer scientist; and also a solid reference for the advanced researcher. I am pleased to say that Probability and Computing...succeeds on all these fronts. I found the book a joy to read: the writing is clear and concise, the proofs are well-structured, and the writing style invites the reader to explore the material. The book is also organized very well, and the selection of topics is excellent. I have already used the book multiple times as a reference, and have found it incredibly useful each time."
Jonathan Katz, Department of Computer Science, University of Maryland for SIGACT News
"...this book is an authoritative and up-to-date reference on the implementation of the simplex method. For the audience of readers who are interested in implementing the simplex method it is a 'must read.'"
Brian Borchers, University of Maryland at College Park, MD
"A well conceived textbook... It is an attractive feature of the book that many concepts are motivated by examples and illustrated with probabilistic algorithms from computer science..."
Harald Niederreiter for Mathematics of Computation
"Mitzenmacher and Upfal have written an excellent introductory textbook on the role of randomness in algorithms and computer simulation. I would recommend it to anyone looking for a fresh approach to the basics of probability."
Max Buot, Carnegie Mellon University, Journal of the American Statistical Association
"The exposition is clear and the development carefully paced and well motivated."
Mark R. Jerrum, Mathematical Reviews
Colin Cooper, King's College, University of London
"An exciting new book on randomized algorithms. It nicely covers all the basics, and also has some interesting modern applications for the more advanced student."
Alan Frieze, Carnegie-Mellon University
"This text provides a solid background in probabilistic techniques, illustrating each with well-chosen examples. The explanations are clear, and convey the intuition behind the results and techniques, yet the coverage is rigorous. An excellent advanced undergraduate text."
Peter Bartlett, University of California, Berkeley
"Probability is part of the conceptual core of modern computer science. Probabilistic analysis of algorithms, randomized algorithms and probabilistic combinatorial constructions have become fundamental tools for computer science and applied mathematics. This book provides a thorough grounding in discrete probability and its applications in computing,at a level accessible to advanced undergraduates in the computational, mathematical and engineering sciences."
Richard M. Karp, University of California, Berkeley
"This text presents a clear exposition of the tools of probabilistic analysis from the very basics to more advanced topics. In addition, each chapter offers a well-chosen set of problems for a range of abilities. This book is suitable for upper division undergraduates and first year graduate students in computer science and related disciplines. It will also be useful as a reference for researchers who would like to incorporate these tools into their work. I enjoyed teaching from the book and highly recommend it."
Valerie King, University of Victoria
"The structure and pace of this book are well matched to the intended audience. The authors consistently maintain a good balance between theory and applications...Good students will be challenged and yet average students will find the text very readable. This is a very attractive textbook."
MAA, Bill Satzer
"The book can be used for self-study since there are exercises in each chapter."
Mathematics of Computation
"Because of the widespread interest in the subject, a textbook covering randomization in computer science must...be many things to many different people: it should serve as an introduction to the area for an undergraduate or graduate student interested in randomized algorithms; a survey of applications and techniques for the general computer scientist; and also a solid reference for the advanced researcher. I am pleased to say that Probability and Computing...succeeds on all these fronts. I found the book a joy to read: the writing is clear and concise, the proofs are well-structured, and the writing style invites the reader to explore the material. The book is also organized very well, and the selection of topics is excellent. I have already used the book multiple times as a reference, and have found it incredibly useful each time."
Jonathan Katz, Department of Computer Science, University of Maryland for SIGACT News
"...this book is an authoritative and up-to-date reference on the implementation of the simplex method. For the audience of readers who are interested in implementing the simplex method it is a 'must read.'"
Brian Borchers, University of Maryland at College Park, MD
"A well conceived textbook... It is an attractive feature of the book that many concepts are motivated by examples and illustrated with probabilistic algorithms from computer science..."
Harald Niederreiter for Mathematics of Computation
"Mitzenmacher and Upfal have written an excellent introductory textbook on the role of randomness in algorithms and computer simulation. I would recommend it to anyone looking for a fresh approach to the basics of probability."
Max Buot, Carnegie Mellon University, Journal of the American Statistical Association
"The exposition is clear and the development carefully paced and well motivated."
Mark R. Jerrum, Mathematical Reviews
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.
Product Details
Would you like to update product info or give feedback on images?
|
More About the Author
Discover books, learn about writers, read author blogs, and more.
Customer Reviews
|
Share your thoughts with other customers:
|
||||||||||||||||||||||
|
Most Helpful Customer Reviews
20 of 21 people found the following review helpful:
5.0 out of 5 stars
Advanced probability topics without measure theory,
By Jung Dalg "Jung" (Bangkok) - See all my reviews
This review is from: Probability and Computing: Randomized Algorithms and Probabilistic Analysis (Hardcover)
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.
17 of 23 people found the following review helpful:
2.0 out of 5 stars
Just unnecessary,
By anon "anon" (USA) - See all my reviews
This review is from: Probability and Computing: Randomized Algorithms and Probabilistic Analysis (Hardcover)
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.
3 of 3 people found the following review helpful:
5.0 out of 5 stars
Accessible, thorough, and reader-friendly,
This review is from: Probability and Computing: Randomized Algorithms and Probabilistic Analysis (Hardcover)
Far from being "unnecessary", this book does a wonderful job at making randomized algorithms accessible and fun. It is great for self-learners because it first motivates a concept, then states relevant theorems, then provides full proofs of these theorems, and then provides an example where the theorem is used. Rather than leaving the proofs of theorems as an "exercise for the reader", it fully proves the theorems stated in the text. Each chapter also includes a section with an interesting application of how the material in the chapter can be utilized. Although not as advanced as the Motwani Randomized Algorithms book, it provides an excellent introduction and is much more accessible.
Share your thoughts with other customers: Create your own review
|
|
Inside This Book
(learn more)
First Sentence:
Computers can sometimes makes mistakes, due for example to incorrect programming or hardware failure. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
balanced allocation paradigm, tth ball, martingale stopping theorem, surviving clause, coupling lemma, local lemma, tails with probability, simple randomized algorithm, probability that the algorithm, unbiased bits, random hash functions, fair coin flips, coupon collector, active packets, median algorithm, second moment method, active balls, butterfly network, geometric random variable, extraction function, stationary distribution, path coupling, satisfying assignment, martingale with respect, head vertex Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Exercises Exercise, Las Vegas, Random Quicksort, Applying Chebyshev, Social Security New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Computers can sometimes makes mistakes, due for example to incorrect programming or hardware failure. Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
balanced allocation paradigm, tth ball, martingale stopping theorem, surviving clause, coupling lemma, local lemma, tails with probability, simple randomized algorithm, probability that the algorithm, unbiased bits, random hash functions, fair coin flips, coupon collector, active packets, median algorithm, second moment method, active balls, butterfly network, geometric random variable, extraction function, stationary distribution, path coupling, satisfying assignment, martingale with respect, head vertex Key Phrases - Capitalized Phrases (CAPs): (learn more)
Monte Carlo, Exercises Exercise, Las Vegas, Random Quicksort, Applying Chebyshev, Social Security New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 10
books:
See all 10 books this book cites
2
books
cite this book:
- Randomized Algorithms by Rajeev Motwani in Front Matter, and Back Matter
- Elements of Information Theory 2nd Edition (Wiley Series in Telecommunications and Signal Processing) by Thomas M. Cover in Back Matter
- The Probabilistic Method (Wiley Series in Discrete Mathematics and Optimization) by Noga Alon in Back Matter
- Probability Models for Computer Science by Sheldon M. Ross in Back Matter
- Ten Lectures on the Probabilistic Method (CBMS-NSF Regional Conference Series in Applied Mathematics) by Joel Spencer in Back Matter
See all 10 books this book cites
- Invitation to Fixed Parameter Algorithms (Oxford Lecture Series in Mathematics and Its Applications) by Rolf Niedermeier in Back Matter
- Proceedings of the Eighth Workshop on Algorithm Engineering and Experiments and the Third Workshop on Analytic Algorithmics and Combinatorics (Proceedings in Applied Mathematics) by Rajeev Raman on page 49
Books on Related Topics
(learn more)
|
What Other Items Do Customers Buy After Viewing This Item?
Tags Customers Associate with This Product(What's this?)Click on a tag to find related items, discussions, and people.
|
Sell a Digital Version of This Book in the Kindle Store
If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store.
Learn more
Customer Discussions
|
This product's forum
Active discussions in related forums
Search Customer Discussions
|
Related forums
|
Listmania!
|
|
So You'd Like to...
|
|
