Isbn: 9781107606609 - concentration of measure for the analysis of randomized algorithms (16 risultati)

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Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. In English.

Condizione: New. This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms. Num Pages: 214 pages, 12 b/w illus. 168 exercises. BIC Classification: PBD; PBT; UMB. Category: (P) Professional & Vocational. Dimension: 227 x 153 x 13. Weight in Grams: 32. . 2012. Illustrated. paperback. . . . . …

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New. pp. 214.

Condizione: New. This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms. Num Pages: 214 pages, 12 b/w illus. 168 exercises. BIC Classification: PBD; PBT; UMB. Category: (P) Professional & Vocational. Dimension: 227 x 153 x 13. Weight in Grams: 32. . 2012. Illustrated. paperback. . . . . Books ship from the US and Ireland. …

Paperback. Condizione: Brand New. 216 pages. 8.90x5.90x0.70 inches. In Stock.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Randomized algorithms have become a central part of the algorithms curriculum, based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high probability estimates on the performance of randomized algorithms. It covers the basic toolkit from the Chernoff-Hoeffding bounds to more sophisticated techniques like martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as Chernoff-Hoeffding bounds in dependent settings. The authors emphasise comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus making the book accessible to computer scientists as well as probabilists and discrete mathematicians.…

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Condizione: New. Print on Demand pp. 214 12 Illus.

Condizione: New. PRINT ON DEMAND pp. 214.

Paperback. Condizione: new. Paperback. Randomized algorithms have become a central part of the algorithms curriculum, based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high probability estimates on the performance of randomized algorithms. It covers the basic toolkit from the ChernoffHoeffding bounds to more sophisticated techniques like martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as ChernoffHoeffding bounds in dependent settings. The authors emphasise comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus making the book accessible to computer scientists as well as probabilists and discrete mathematicians. This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms.&Uumlber den AutorDevdatt P. Dubhashi is Professor in the Department of Computer Scie.…