1. Chernoff–Hoeffding bounds; 2. Applying the CH-bounds; 3. CH-bounds with dependencies; 4. Interlude: probabilistic recurrences; 5. Martingales and the MOBD; 6. The MOBD in action; 7. Averaged bounded difference; 8. The method of bounded variances; 9. Interlude: the infamous upper tail; 10. Isoperimetric inequalities and concentration; 11. Talagrand inequality; 12. Transportation cost and concentration; 13. Transportation cost and Talagrand's inequality; 14. Log–Sobolev inequalities; Appendix A. Summary of the most useful bounds.
This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms.
Devdatt P. Dubhashi is Professor in the Department of Computer Science and Engineering at Chalmers University, Sweden. He earned a Ph.D. in computer science from Cornell University and held positions at the Max-Planck-Institute for Computer Science in Saarbruecken, BRICS, the University of Aarhus and IIT Delhi. Dubhashi has published widely at international conferences and in journals, including many special issues dedicated to best contributions. His research interests span the range from combinatorics to probabilistic analysis of algorithms, and more recently, to computational systems biology and distributed information systems such as the Web. Alessandro Panconesi is Professor of Computer Science at Sapienza University of Rome. He earned a Ph.D. in computer science from Cornell University and is the recipient of the 1992 ACM Danny Lewin Award. Panconesi has published more than 50 papers in international journals and selective conference proceedings and he is the associate editor of the Journal of Discrete Algorithms and the director of BiCi, the Bertinoro International Center of Informatics. His research spans areas of algorithmic research as diverse as randomized algorithms, distributed computing, complexity theory, experimental algorithmics, wireless networking and Web information retrieval.
Review of the hardback: 'It is beautifully written, contains all
the major concentration results, and is a must to have on your
desk.' Richard Lipton
Review of the hardback: 'Concentration bounds are at the core of
probabilistic analysis of algorithms. This excellent text provides
a comprehensive treatment of this important subject, ranging from
the very basic to the more advanced tools, including some recent
developments in this area. The presentation is clear and includes
numerous examples, demonstrating applications of the bounds in
analysis of algorithms. This book is a valuable resource for both
researchers and students in the field.' Eli Upfal, Brown
University
Review of the hardback: 'Concentration inequalities are an
essential tool for the analysis of algorithms in any probabilistic
setting. There have been many recent developments on this subject,
and this excellent text brings them together in a highly accessible
form.' Alan Frieze, Carnegie Mellon University
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Review of the hardback: '… the strength of this book is that it is
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Yannis C. Stamatiou, Mathematical Reviews
Reviews of the hardback: 'This timely book brings together in a
comprehensive and accessible form a sophisticated toolkit of
powerful techniques for the analysis of randomized algorithms,
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This book is an invaluable resource for people venturing into this
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Prabhakar Ragahavan, Yahoo Research
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