Title :
Entropy power inequality for a family of discrete random variables
Author :
Sharma, Naresh ; Das, Smarajit ; Muthukrishnan, Siddharth
Author_Institution :
Sch. of Technol. & Comput. Sci., Tata Inst. of Fundamental Res., Mumbai, India
fDate :
July 31 2011-Aug. 5 2011
Abstract :
It is known that the Entropy Power Inequality (EPI) always holds if the random variables have density. Not much work has been done to identify discrete distributions for which the inequality holds with the differential entropy replaced by the discrete entropy. Harremoës and Vignat showed that it holds for the pair (B(m, p),B(n, p)), m, n ∈ ℕ, (where B(n, p) is a Binomial distribution with n trials each with success probability p) for p = 0.5. In this paper, we considerably expand the set of Binomial distributions for which the inequality holds and, in particular, identify n0(p) such that for all m, n ≥ n0(p), the EPI holds for (B(m, p),B(n, p)). We further show that the EPI holds for the discrete random variables that can be expressed as the sum of n independent and identically distributed (IID) discrete random variables for large n.
Keywords :
binomial distribution; entropy; random processes; binomial distribution; differential entropy; discrete distributions; entropy power inequality; identically distributed discrete random variables; Context; Convolution; Entropy; Indexes; Information theory; Random variables; Upper bound; Entropy power inequality; Taylor´s theorem; asymptotic series; binomial distribution;
Conference_Titel :
Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on
Conference_Location :
St. Petersburg
Print_ISBN :
978-1-4577-0596-0
Electronic_ISBN :
2157-8095
DOI :
10.1109/ISIT.2011.6033891