DocumentCode :
639963
Title :
The Entropy Power Inequality and Mrs. Gerber´s Lemma for groups of order 2n
Author :
Jog, V. ; Anantharam, Venkat
Author_Institution :
EECS, UC Berkeley, Berkeley, CA, USA
fYear :
2013
fDate :
7-12 July 2013
Firstpage :
594
Lastpage :
598
Abstract :
Shannon´s Entropy Power Inequality (EPI) can be viewed as characterizing the minimum differential entropy achievable by the sum of two independent random variables with fixed differential entropies. The EPI is a powerful tool and has been used to resolve a number of problems in information theory. In this paper we examine the existence of a similar entropy inequality for discrete random variables. We obtain an entropy power inequality for random variables taking values in any group of order 2n, i.e. for such a group G we explicitly characterize the function fG(x, y) giving the minimum entropy of the group product of two independent G-valued random variables with respective entropies x and y. Random variables achieving the extremum in this inequality are thus the analogs of Gaussians, and these are also determined. It turns out that fG(x, y) is convex in x for fixed y and, by symmetry, convex in y for fixed x. This is a generalization to groups of order 2n of the result known as Mrs. Gerber´s Lemma.
Keywords :
group theory; information theory; EPI; G-valued random variables; Gerber´s lemma; Shannon entropy power inequality; discrete random variables; fixed differential entropies; group product; groups; independent random variables; information theory; minimum differential entropy; similar entropy inequality; Convolution; Covariance matrices; Electronic mail; Entropy; Equations; Information theory; Random variables; Entropy; Entropy power inequality; Finite groups; Mrs. Gerber´s Lemma;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Theory Proceedings (ISIT), 2013 IEEE International Symposium on
Conference_Location :
Istanbul
ISSN :
2157-8095
Type :
conf
DOI :
10.1109/ISIT.2013.6620295
Filename :
6620295
Link To Document :
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