Probability and computing : randomized algorithms and probabilistic analysis Michael Mitzenmacher; Eli Upfal

By:

Mitzenmacher, Michael

Contributor(s):

Upfal, Eli

Material type: Text

TextPublication details: Cambridge, United Kingdom ; New York, NY : Cambridge University Press, 2017.Edition: 2nd edDescription: xx, 467 pages : illustrations ; 27 cmISBN:

9781107154889

Subject(s):

DDC classification:

518.1 MIT

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. 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

List(s) this item appears in: New Arrivals January March 2022

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Holdings
Item type	Current library	Collection	Call number	Status	Barcode
Reference	IIITDM Kurnool Reference	Reference	518.1 MIT (Browse shelf(Opens below))	Not For Loan	0004296
Books	IIITDM Kurnool General Stacks	Non-fiction	518.1 MIT (Browse shelf(Opens below))	Available	0004297
Books	IIITDM Kurnool General Stacks	Non-fiction	518.1 MIT (Browse shelf(Opens below))	Available	0004298
Books	IIITDM Kurnool General Stacks	Non-fiction	518.1 MIT (Browse shelf(Opens below))	Available	0004299
Books	IIITDM Kurnool General Stacks	Non-fiction	518.1 MIT (Browse shelf(Opens below))	Available	0004300

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.

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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