This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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.
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.
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.
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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
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