Abstract
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…
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2Topics & keywords
Keywords
- Probabilistic logic
- Computer science
- Markov chain
- Theoretical computer science
- Independence (probability theory)
- Chernoff bound
- Markov process
- Markov chain Monte Carlo
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