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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Journal d Analyse Ma...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Journal d Analyse Mathématique
Article . 1965 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
zbMATH Open
Article . 1965
Data sources: zbMATH Open
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Probabilistic methods in group theory

Authors: Erdős, Paul; Rényi, Alfréd;

Probabilistic methods in group theory

Abstract

Depuis une trentaine d'années, les méthodes probabilistes trouvent d'intéressantes applications dans divers domaines des mathématiques: analyse, théorie des graphes, théorie des nombres. C'est l'illustre mathématicien russe Yu. V. Linnik qui, sous le nom de méthode de dispersion, a fait des méthodes probabilistes un puissant instrument d'investigation dans la théorie des nombres. Des méthodes probabilistes permettent de résoudre de nombreux problèmes qu'on ne sait pas traiter par d'autres méthodes et qui cependant, à première vue, n'ont rien à voir avec le hasard. Les AA. donnent d'intéressantes applications des méthodes probabilistes à la théorie des groupes abéliens finis. Soit \(G_n\) un groupe abélien additif d'ordre \(n\) et soient \(a,b,c,\ldots\) les éléments de ce groupe. On pose \(1 \cdot a=a\) et \(0 \cdot a=0\) (élément neutre de \(G_n\)), quel que soit l'élément à de \(G_n\). Soient \(a_1, \ldots ,a_k\) \(k\) éléments de \(G_n\) choisis au hasard indépendamment les uns des autres, la probabilité de choisir \(a_i\) étant \(1/n\), quel que soit \(i\). Soit \(V_k(b)\) le nombre de représentations d'un élément \(b\) de \(G_n\), de la forme \(b= \varepsilon_1a_1+ \varepsilon_2a_2+ \ldots + \varepsilon_ka_k\), où chacun des nombres \(\varepsilon_i\) peut prendre l'une des valeurs 0 ou 1. Pur tout \(b \in G_n\), \(V_k(b)\) est une variable aléatoire (v.a.). Si \(a_1, \ldots ,a_k\) sont fixes, on a \(\sum_{b \in G_n} V_k(b)=2^k\) et \(\left\{\frac{V_k(b)}{2^k} \right\}\) est une loi de probabilités. Conformément à l'usage, les AA. désignent par la symbole \(P(\ldots)\) la probabilité de l'événement indiqué entre parenthèses et par \(E(\ldots)\) la valeur moyenne de la v.a. indiquée entre parenthèses. Cela posé, les AA. demontrent les deux théorèmes suivants: 1. Si \(k \geq \frac{2 \log n+2 \log 1/ \varepsilon + \log 1/\delta}{\log 2}\) où \(\varepsilon >0\) et \(\delta > 0\) sont des nombres positifs aussi petits que l'on veut, alors \[ P \left(\text{Max}_{b \in G_n} \left|V_k(b)-\frac{2^k}{n}\right| \leq \varepsilon \frac{2^k}{n} \right) >1 - \delta. \] 2. Pour tout \(\delta >0\), si \(k \geq \frac{\log n+2 \log 1/ \delta + \log (\log n/ \log 2)}{\log 2}+5\) alors \[ P \left(\text{Min}_{b \in G_n} V_k(b) > 0 \right) > 1 - \delta. \]

Related Organizations
Keywords

finite abelian groups, Probabilistic methods in group theory, Probability theory on algebraic and topological structures, probabilistic methods

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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!
47
Top 10%
Top 1%
Average
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