文档介绍：Probability puting
Randomized Algorithms and Probabilistic Analysis
Michael Mitzenmacher
• Eli Upfal
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Probability puting
Randomization and probabilistic techniques play an important role in •
puter science, with applications ranging binatorial optimization and machine
learning works and secure protocols.
This textbook is designed to pany a one- or two-semester course for advanced
undergraduates or beginning graduate students puter science and applied mathe•
matics. 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, expec•
tations, Markov's inequality, Chebyshev's inequality, ChernotT bounds, balls-and-bins
models, the probabilistic method, and Markov chains. In the second half, the authors
delve into more advanced topics such as continuous probability, applications of limited
independence, entropy, Markov chain Monte Carlo methods. coupling, martingales,
and balanced allocations. With prehensive selection of topics, along with many
examples and exercises, this book is an indispensable teaching tool.
Michael Mitzenmacher is John L. Loeb Associate Professor puter Science at
Harvard University. He received his . from the University of California. Berke•
ley, in 1996. Prior to joining Harvard in 1999, he was a research staff member at Digital
Systems Research Laboratory in Palo Alto. He has received an NSF CAREER Award
and an Alfred P. Sloan Research Fellowship. In 2002, he shared the IEEE Information
Theory Society "Best Paper" Award for his work on error-correcting codes.
Eli Upfal is Professor and Chair puter Science at Brown University. He received
his