Probability and Computing
eBook - PDF

Probability and Computing

Randomization and Probabilistic Techniques in Algorithms and Data Analysis

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Probability and Computing

Randomization and Probabilistic Techniques in Algorithms and Data Analysis

About this book

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.

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Yes, you can access Probability and Computing by Michael Mitzenmacher,Eli Upfal in PDF and/or ePUB format, as well as other popular books in Computer Science & Programming Algorithms. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half title
  3. Title
  4. Copyright
  5. Dedication
  6. Contents
  7. Preface to the Second Edition
  8. Preface to the First Edition
  9. 1 Events and Probability
  10. 2 Discrete Random Variables and Expectation
  11. 3 Moments and Deviations
  12. 4 Chernoff and Hoeffding Bounds
  13. 5 Balls, Bins, and Random Graphs
  14. 6 The Probabilistic Method
  15. 7 Markov Chains and Random Walks
  16. 8 Continuous Distributions and the Poisson Process
  17. 9 The Normal Distribution
  18. 10 Entropy, Randomness, and Information
  19. 11 The Monte Carlo Method
  20. 12 Coupling of Markov Chains
  21. 13 Martingales
  22. 14 Sample Complexity, VC Dimension, and Rademacher Complexity
  23. 15 Pairwise Independence and Universal Hash Functions
  24. 16 Power Laws and Related Distributions
  25. 17 Balanced Allocations and Cuckoo Hashing
  26. Further Reading
  27. Index