Title :
A tight upper bound on discrete entropy
Author_Institution :
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
fDate :
3/1/1998 12:00:00 AM
Abstract :
The standard upper bound on discrete entropy was derived based on the differential entropy bound for continuous random variables. A tighter discrete entropy bound is derived using the transformation formula of Jacobi theta function. The new bound is applicable only when the probability mass function of the discrete random variable satisfies certain conditions. Its application to the class of binomial random variables is presented as an example
Keywords :
entropy; functional equations; probability; random processes; Jacobi theta function; binomial random variables; continuous random variables; differential entropy bound; discrete entropy; discrete random variable; probability mass function; tight upper bound; transformation formula; Entropy; Information theory; Jacobian matrices; Random variables; Upper bound;
Journal_Title :
Information Theory, IEEE Transactions on
