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Desigualtats de concentració

Authors: Lugosi, Gábor;

Desigualtats de concentració

Abstract

Les lleis dels grans nombres en la teoria clàssica de probabilitats asseguren que la suma de variables aleatòries independents es troba, sota certes condicions febles, molt a prop del seu valor esperat amb alta probabilitat. Aquestes sumes són l'exemple més senzill de variables aleatòries concentrades al voltant de la seva mitjana. Alguns resultats més recents revelen que aquest comportament és compartit per una immensa classe de funcions de variables aleatòries independents. Aquests resultats es coneixen generalment com a desigualtats de concentració. El propòsit d'aquest article és oferir una introducció a algunes d'aquestes desigualtats.

The laws of large numbers of classical probability theory state that sums of independent random variables are, under very mild conditions, close to their expected value with large probability. Such sums are the most basic examples of random variables concentrated around their mean. More recent results reveal that such a behavior is shared by a large class of general functions of independent random variables. Such results go generally under the name of “concentration inequalities.” The purpose of this article is to offer an introduction to some of these inequalities.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green