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Probability and computing : randomisation and probabilistic techniques in algorithms and data analysis

By: Contributor(s): Material type: TextTextLanguage: English Publication details: New York Cambridge University Press 2005Edition: 2nd edDescription: xx, 462pISBN:
  • 9781107154889 (HB)
Subject(s):
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.
Summary: 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.
Item type: BOOKS List(s) this item appears in: New Arrivals (01 March 2026)
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Home library Call number Materials specified Status Notes Date due Barcode
IMSc Library 519.21 MIT (Browse shelf(Opens below)) Not For Loan New Arrival Display upto 15 March 2026 78926

Includes index

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.

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.

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The Institute of Mathematical Sciences, Chennai, India