Probability and computing : randomization and probabilistic techniques in algorithms and data analysis
著者
書誌事項
Probability and computing : randomization and probabilistic techniques in algorithms and data analysis
Cambridge University Press, 2017
2nd ed
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
- 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.
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