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Probability and Computing: Randomized Algorithms and Probabilistic Analysis ハードカバー – 2005/1/31
英語版
Michael Mitzenmacher
(著),
Eli Upfal
(著)
この商品には新版があります:
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. This 2005 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. It assumes only an elementary background in discrete mathematics and gives a rigorous yet accessible treatment of the material, with numerous examples and applications. The first half of the book covers core material, including random sampling, expectations, Markov's inequality, Chevyshev's inequality, Chernoff bounds, the probabilistic method and Markov chains. The second half covers more advanced topics such as continuous probability, applications of limited independence, entropy, Markov chain Monte Carlo methods and balanced allocations. With its comprehensive selection of topics, along with many examples and exercises, this book is an indispensable teaching tool.
- 本の長さ370ページ
- 言語英語
- 出版社Cambridge University Press
- 発売日2005/1/31
- 寸法17.78 x 2.54 x 25.4 cm
- ISBN-100521835402
- ISBN-13978-0521835404
商品の説明
レビュー
'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, Professor of Computer Science, University of California, Berkeley
'This book is suitable as a text 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, Professor of Computer Science, University of Victoria, British Columbia
'Buy it, read it, enjoy it; profit from it. it feels as if it has been well tested out of students and will work straight away.' Colin Cooper, Department of computer Science, 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, professor of Mathematics, Carnegie-Mellon University
' … very well written and contains useful material on probability theory and its application in computer science.' Zentralblatt MATH
' … this book offers a very good introduction to randomised algorithms and probabilistic analysis, both for the lecturer and independent reader alike. it is also a good book for those wanting practical examples that can be applied to real world problems.' Mathematics Today
'This book is suitable as a text 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, Professor of Computer Science, University of Victoria, British Columbia
'Buy it, read it, enjoy it; profit from it. it feels as if it has been well tested out of students and will work straight away.' Colin Cooper, Department of computer Science, 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, professor of Mathematics, Carnegie-Mellon University
' … very well written and contains useful material on probability theory and its application in computer science.' Zentralblatt MATH
' … this book offers a very good introduction to randomised algorithms and probabilistic analysis, both for the lecturer and independent reader alike. it is also a good book for those wanting practical examples that can be applied to real world problems.' Mathematics Today
著者について
Michael Miztenmacher is a John L. Loeb Associate Professor in Computer Science at Harvard University. Having written nearly 100 articles on a variety of topics in computer science, his research focuses on randomized algorithms and networks. He has received an NSF CAREER Award and an Alfred P. Sloan Research Fellowship. In 2002, he shared the IEEE Information Theory Society Best Paper Award for his work on error-correcting codes.
Eli Upfal is Professor and Chair of Computer Science at Brown University. He has published more than 100 papers in refereed journals and professional conferences, and is the inventor of more than ten patents. His main research interests are randomized computation and probabilistic analysis of algorithms, with applications to optimization algorithms, communication networks, parallel and distributed computing and computational biology.
Eli Upfal is Professor and Chair of Computer Science at Brown University. He has published more than 100 papers in refereed journals and professional conferences, and is the inventor of more than ten patents. His main research interests are randomized computation and probabilistic analysis of algorithms, with applications to optimization algorithms, communication networks, parallel and distributed computing and computational biology.
登録情報
- 出版社 : Cambridge University Press (2005/1/31)
- 発売日 : 2005/1/31
- 言語 : 英語
- ハードカバー : 370ページ
- ISBN-10 : 0521835402
- ISBN-13 : 978-0521835404
- 寸法 : 17.78 x 2.54 x 25.4 cm
- Amazon 売れ筋ランキング: - 474,633位洋書 (洋書の売れ筋ランキングを見る)
- - 1,183位Professional & Technical Mathematical Analysis
- - 1,261位Mathematical Analysis (洋書)
- - 1,376位Algebra
- カスタマーレビュー:
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他の国からのトップレビュー
A. Smola
5つ星のうち5.0
Instant classic
2015年11月1日にアメリカ合衆国でレビュー済みAmazonで購入
A classic. Lots of deep material is explained beautifully here, tricking the student to think that this is easy and simple (while it obviously isn't). This is how all textbooks should be written. Recommended reading for anyone studying computer science. And statisticians, too.
Rebecca
5つ星のうち5.0
It is perfect!!
2013年3月3日に英国でレビュー済みAmazonで購入
It is a perfect-look book! It is my required text book. The book is very typical and useful. Recommend it !
Amazon Customer
5つ星のうち5.0
awesome book
2019年8月27日にアメリカ合衆国でレビュー済みAmazonで購入
awesome book with great explanations and excercises
MA
5つ星のうち4.0
Great book for a clean introduction to (advanced) discrete probability
2013年4月15日にアメリカ合衆国でレビュー済みAmazonで購入
This book is a really nice introduction to probability (graduate level).
The material is presented in a way appealing to an engineer; the authors
- describe concepts (and provide intuition) that are motivated (derived) by applications in computer science and electrical engineering,
- restrict themselves to the presentation of discrete problems (e.g. settings where there are finite/countable number of variables, and finite/countable domains etc) whose presentation is cleaner and easily digested by the reader who need not have an advanced math background.
- omit details (e.g. in definitions) that would probably be required to make a statement formally correct, but are meaningless in the problems encountered in real applications.
I definitely suggest the book as a starting point to any young graduate student who wants to quickly familiarize with a wide range of important concepts in (discrete) probability without having to worry about frustrating details, extreme cases and notation.
The material is presented in a way appealing to an engineer; the authors
- describe concepts (and provide intuition) that are motivated (derived) by applications in computer science and electrical engineering,
- restrict themselves to the presentation of discrete problems (e.g. settings where there are finite/countable number of variables, and finite/countable domains etc) whose presentation is cleaner and easily digested by the reader who need not have an advanced math background.
- omit details (e.g. in definitions) that would probably be required to make a statement formally correct, but are meaningless in the problems encountered in real applications.
I definitely suggest the book as a starting point to any young graduate student who wants to quickly familiarize with a wide range of important concepts in (discrete) probability without having to worry about frustrating details, extreme cases and notation.