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Probability and Computing
Randomization and Probabilistic Techniques in Algorithms and Data Analysis

2nd Edition

$69.99 (X)

  • Date Published: July 2017
  • availability: In stock
  • format: Hardback
  • isbn: 9781107154889

$ 69.99 (X)

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About the Authors
  • Greatly expanded, this new edition requires only an elementary background in discrete mathematics and offers a comprehensive introduction to the role of randomization and probabilistic techniques in modern computer science. Newly added chapters and sections cover topics including normal distributions, sample complexity, VC dimension, Rademacher complexity, power laws and related distributions, cuckoo hashing, and the Lovasz Local Lemma. Material relevant to machine learning and big data analysis enables students to learn modern techniques and applications. Among the many new exercises and examples are programming-related exercises that provide students with excellent training in solving relevant problems. This book provides an indispensable teaching tool to accompany a one- or two-semester course for advanced undergraduate students in computer science and applied mathematics.

    • Contains all the background in probability needed to understand many subdisciplines of computer science
    • Includes new material relevant to machine learning and big data analysis, enabling students to learn new, up-to-date techniques and applications
    • Newly added chapters and sections cover the normal distribution, sample complexity, VC dimension, naïve Bayes, cuckoo hashing, power laws, and the Lovasz Local Lemma
    • Many new exercises and examples, including several new programming-related exercises, provide students with excellent training in problem solving
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    Reviews & endorsements

    'As randomized methods continue to grow in importance, this textbook provides a rigorous yet accessible introduction to fundamental concepts that need to be widely known. The new chapters in this second edition, about sample size and power laws, make it especially valuable for today's applications.' Donald E. Knuth, Stanford University

    'Of all the courses I have taught at Berkeley, my favorite is the one based on the Mitzenmacher-Upfal book Probability and Computing. Students appreciate the clarity and crispness of the arguments and the relevance of the material to the study of algorithms. The new Second Edition adds much important material on continuous random variables, entropy, randomness and information, advanced data structures and topics of current interest related to machine learning and the analysis of large data sets.' Richard M. Karp, University of California, Berkeley

    'The new edition is great. I’m especially excited that the authors have added sections on the normal distribution, learning theory and power laws. This is just what the doctor ordered or, more precisely, what teachers such as myself ordered!' Anna Karlin, University of Washington

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    Product details

    • Edition: 2nd Edition
    • Date Published: July 2017
    • format: Hardback
    • isbn: 9781107154889
    • length: 484 pages
    • dimensions: 260 x 182 x 25 mm
    • weight: 1.15kg
    • contains: 8 b/w illus. 1 table
    • availability: In stock
  • Table of Contents

    1. Events and probability
    2. Discrete random variables and expectations
    3. Moments and deviations
    4. Chernoff and Hoeffding bounds
    5. Balls, bins, and random graphs
    6. The probabilistic method
    7. Markov chains and random walks
    8. Continuous distributions and the Polsson process
    9. The normal distribution
    10. Entropy, randomness, and information
    11. The Monte Carlo method
    12. Coupling of Markov chains
    13. Martingales
    14. Sample complexity, VC dimension, and Rademacher complexity
    15. Pairwise independence and universal hash functions
    16. Power laws and related distributions
    17. Balanced allocations and cuckoo hashing.

  • Authors

    Michael Mitzenmacher, Harvard University, Massachusetts
    Michael Mitzenmacher is a Professor of Computer Science in the School of Engineering and Applied Sciences at Harvard University, Massachusetts. Professor Mitzenmacher has authored or co-authored over 200 conference and journal publications on a variety of topics, including algorithms for the internet, efficient hash-based data structures, erasure and error-correcting codes, power laws, and compression. His work on low-density parity-check codes shared the 2002 IEEE Information Theory Society Best Paper Award and won the 2009 ACM SIGCOMM Test of Time Award. He was elected as the Chair of the ACM Special Interest Group on Algorithms and Computation Theory in 2015.

    Eli Upfal, Brown University, Rhode Island
    Eli Upfal is a Professor of Computer Science at Brown University, where he was also the department chair from 2002 to 2007. Prior to joining Brown in 1998, he was a researcher and project manager at the IBM Almaden Research Center, and a professor at the Weizmann Institute of Science, Israel. His main research interests are randomized algorithms, probabilistic analysis of algorithms, and computational statistics, with applications ranging from combinatorial and stochastic optimization, massive data analysis and sampling complexity to computational biology, and computational finance.

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