TY  - BOOK
AU  - Mitzenmacher, Michael
AU  - Upfal, Eli
TI  - Probability and computing : : randomized algorithms and probabilistic analysis
SN  - 9781107154889
U1  - 518.1 
PY  - 2017///
CY  - Cambridge, United Kingdom ; New York, NY : 
PB  - Cambridge University Press, 
KW  - Computer science--Mathematics
KW  - Probabilities
KW  - Stochastic analysis
KW  - Algorithms
N1  - 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
N2  - 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
ER  -