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商品コード: 9781107154889

Probability and Computing

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書名

Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis, 2nd Edition
著者・編者 Mitzenmacher, M. & Upfal, E.
出版社/発行元 Cambridge University Press
発行年/月 2017年7月   
装丁 Hardcover
ページ数/巻数 488 ページ
ISBN 978-1-107-15488-9
発送予定 海外倉庫よりお取り寄せ 2-3週間以内に発送します

 

Desciption

 

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, naive Bayes, cuckoo hashing, power laws, and the Lovasz Local Lemma
- Many new exercises and examples, including several new programming-related exercises, providing students with excellent training in problem solving

 

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

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