000			02889nam a22002417a 4500
999			_c1422 _d1422
005			20220317151325.0
008			220317b ||||| |||| 00| 0 eng d
020			_a9781107154889
082			_a518.1 _bMIT
100			_aMitzenmacher, Michael
245			_aProbability and computing : _brandomized algorithms and probabilistic analysis _cMichael Mitzenmacher; Eli Upfal
250			_a2nd ed.
260			_aCambridge, United Kingdom ; New York, NY : _bCambridge University Press, _c2017.
300			_axx, 467 pages : _billustrations ; _c27 cm.
505			_t1. Events and probability _t2. Discrete random variables and expectations _t3. Moments and deviations _t4. Chernoff and Hoeffding bounds _t5. Balls, bins, and random graphs _t6. The probabilistic method _t7. Markov chains and random walks _t8. Continuous distributions and the Polsson process _t9. The normal distribution _t10. Entropy, randomness, and information _t11. The Monte Carlo method _t12. Coupling of Markov chains _t13. Martingales _t14. Sample complexity, VC dimension, and Rademacher complexity _t15. Pairwise independence and universal hash functions _t16. Power laws and related distributions _t17. Balanced allocations and cuckoo hashing.
520			_aGreatly 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
650			_aComputer science--Mathematics
650			_aProbabilities
650			_aStochastic analysis
650			_aAlgorithms
700			_aUpfal, Eli
942			_2ddc _cBK