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Probability and Computing
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  • Cited by 719
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Vani, K. A. Prathima Mabel, J. and Rama Mohan Babu, K. N. 2019. Data Analytics and Learning. Vol. 43, Issue. , p. 39.

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    Looz, Moritz Von and Meyerhenke, Henning 2018. Updating Dynamic Random Hyperbolic Graphs in Sublinear Time. Journal of Experimental Algorithmics, Vol. 23, Issue. 1, p. 1.

    Zhang, Xiangyu Bashizade, Ramin LaBoda, Craig Dwyer, Chris and Lebeck, Alvin R. 2018. Architecting a Stochastic Computing Unit with Molecular Optical Devices. p. 301.

    Damaschke, Peter and Schliep, Alexander 2018. SOFSEM 2018: Theory and Practice of Computer Science. Vol. 10706, Issue. , p. 525.

    Rager, Scott T. Ciftcioglu, Ertugrul N. Ramanathan, Ram La Porta, Thomas F. and Govindan, Ramesh 2018. Scalability and Satisfiability of Quality-of-Information in Wireless Networks. IEEE/ACM Transactions on Networking, Vol. 26, Issue. 1, p. 398.

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    Avin, Chen Koucký, Michal and Lotker, Zvi 2018. Cover time and mixing time of random walks on dynamic graphs. Random Structures & Algorithms, Vol. 52, Issue. 4, p. 576.

    Grubbs, Paul Lacharite, Marie-Sarah Minaud, Brice and Paterson, Kenneth G. 2018. Pump up the Volume. p. 315.

    Lehre, Per Kristian and Oliveto, Pietro S. 2018. Handbook of Heuristics. p. 849.

    Censor-Hillel, Keren Fischer, Eldar Schwartzman, Gregory and Vasudev, Yadu 2018. Fast distributed algorithms for testing graph properties. Distributed Computing,

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Book description

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.

Reviews

'This text provides a solid background in probabilistic techniques, illustrating each with well-chosen examples. The explanations are clear, and convey the intuition behind the results and techniques, yet the coverage is rigorous. An excellent advanced undergraduate text.'

Peter Bartlett - Professor of Computer Science, University of California, Berkeley

'This book is suitable as a text for upper division undergraduates and first year graduate students in computer science and related disciplines. It will also be useful as a reference for researchers who would like to incorporate these tools into their work. I enjoyed teaching from the book and highly recommend it.'

Valerie King - Professor of Computer Science, University of Victoria, British Columbia

'Buy it, read it, enjoy it; profit from it. it feels as if it has been well tested out of students and will work straight away.'

Colin Cooper - Department of computer Science, King's College, University of London

'An exciting new book on randomized algorithms. It nicely covers all the basics, and also has some interesting modern applications for the more advanced student.’

Alan Frieze - professor of Mathematics, Carnegie-Mellon University

' … very well written and contains useful material on probability theory and its application in computer science.'

Source: Zentralblatt MATH

' … this book offers a very good introduction to randomised algorithms and probabilistic analysis, both for the lecturer and independent reader alike. it is also a good book for those wanting practical examples that can be applied to real world problems.'

Source: Mathematics Today

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