Probability and Computing: Randomized Algorithms and Probabilistic Analysis

Mitzenmacher, Michael; Upfal, Eli

ISBN 10: 0521835402 / ISBN 13: 9780521835404

Published by Cambridge University Press, 2005

About this Item

Seller Inventory # CF48

Bibliographic Details

• Title: Probability and Computing: Randomized Algorithms and Probabilistic Analysis
• Publisher: Cambridge University Press
• Publication Date: 2005
• Language: English
• ISBN 10: 0521835402
• ISBN 13: 9780521835404
• Binding: Hardcover
• Condition: New

About this title

Synopsis

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.

About the Authors

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.