Probability and Computing
eBook - PDF

Probability and Computing

Randomized Algorithms and Probabilistic Analysis

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

Probability and Computing

Randomized Algorithms and Probabilistic Analysis

About this book

Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communication networks and secure protocols. This 2005 textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It assumes only an elementary background in discrete mathematics and gives a rigorous yet accessible treatment of the material, with numerous examples and applications. The first half of the book covers core material, including random sampling, expectations, Markov's inequality, Chevyshev's inequality, Chernoff bounds, the probabilistic method and Markov chains. The second half covers more advanced topics such as continuous probability, applications of limited independence, entropy, Markov chain Monte Carlo methods and balanced allocations. With its comprehensive selection of topics, along with many examples and exercises, this book is an indispensable teaching tool.

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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 & Computer Science General. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Title
  3. Copyright
  4. Contents
  5. Preface
  6. 1 Events and Probability
  7. 2 Discrete Random Variables and Expectation
  8. 3 Moments and Deviations
  9. 4 Chernoff Bounds
  10. 5 Balls, Bins, and Random Graphs
  11. 6 The Probabilistic Method
  12. 7 Markov Chains and Random Walks
  13. 8 Continuous Distributions and the Poisson Process
  14. 9 Entropy, Randomness, and Information
  15. 10 The Monte Carlo Method
  16. 11 Coupling of Markov Chains
  17. 12 Martingales
  18. 13 Pairwise Independence and Universal Hash Functions
  19. 14 Balanced Allocations
  20. Further Reading
  21. Index