000			03155nam a22003858i 4500
001			CR9780511581274
003			UkCbUP
005			20200124160325.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			090604s2009||||enk o ||1 0|eng|d
020			_a9780511581274 (ebook)
020			_z9780521884273 (hardback)
020			_z9781107606609 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA273 _b.D765 2009
082	0	0	_a518/.1 _222
100	1		_aDubhashi, Devdatt, _eauthor.
245	1	0	_aConcentration of measure for the analysis of randomized algorithms / _cDevdatt Dubhashi, Alessandro Panconesi.
264		1	_aCambridge : _bCambridge University Press, _c2009.
300			_a1 online resource (xiv, 196 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	0		_aChernoff-Hoeffding bounds -- Applications of the Chernoff-Hoeffding bounds -- Chernoff-Hoeffding bounds in dependent settings -- Interlude : probabilistic recurrences -- Martingales and the method of bounded differences -- The simple method of bounded differences in action -- The method of averaged bounded differences -- The method of bounded variances -- Interlude : the infamous upper tail -- Isoperimetric inequalities and concentration -- Talagrand's isoperimetric inequality -- Isoperimetric inequalities and concentration via transportation cost inequalities -- Quadratic transportation cost and Talagrand's inequality -- Log-Sobolev inequalities and concentration -- Appendix A : summary of the most useful bounds.
520			_aRandomized 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.
650		0	_aRandom variables.
650		0	_aDistribution (Probability theory)
650		0	_aLimit theorems (Probability theory)
650		0	_aAlgorithms.
700	1		_aPanconesi, Alessandro, _eauthor.
776	0	8	_iPrint version: _z9780521884273
856	4	0	_uhttps://doi.org/10.1017/CBO9780511581274
999			_c522172 _d522170