Concentration inequalities: a nonasymptotic theory of independence

An accessible account of the rich theory surrounding concentration inequalities in probability theory, with applications from machine learning and statistics to high-dimensional geometry. This book introduces key ideas and presents a detailed summary of the state-of-the-art in the area making it ide...

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Bibliographische Detailangaben
Hauptverfasser: Boucheron, Stéphane (VerfasserIn), Lugosi, Gábor 1964- (VerfasserIn), Massart, Pascal (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Oxford Oxford University Press 2013
Ausgabe:First edition
Schlagworte:
Online-Zugang:DE-1046
DE-1047
DE-703
DE-739
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Zusammenfassung:An accessible account of the rich theory surrounding concentration inequalities in probability theory, with applications from machine learning and statistics to high-dimensional geometry. This book introduces key ideas and presents a detailed summary of the state-of-the-art in the area making it ideal for independent learning and as a reference
Beschreibung:1 Online-Ressource (X, 481 Seiten) Diagramme
ISBN:9780191747106
9781299160057
9780191655500
DOI:10.1093/acprof:oso/9780199535255.001.0001

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