Partition-Symmetrical Entropy Functions
Abstract
Let . The entropy function of a set of discrete random variables is a -dimensional vector whose entries are , the (joint) entropies of the subsets of the set of random variables with by convention. The set of all entropy functions for discrete random variables, denoted by , is called the entropy function region for . Characterization of and its closure are well-known open problems in information theory. They are important not only because they play key roles in information theory problems but also they are related to other subjects in mathematics and physics. In this paper, we consider \emph{partition-symmetrical entropy functions}. Let be a -partition of . An entropy function is called -symmetrical if for all , whenever , . The set of all the -symmetrical entropy functions, denoted by , is called -symmetrical entropy function region. We prove that , the closure of , is completely characterized by Shannon-type information inequalities if and only if is the -partition or a -partition with one of its blocks being a singleton. The characterization of the partition-symmetrical entropy functions can be useful for solving some information theory and related problems where symmetry exists in the structure of the problems. Keywords: entropy, entropy function, information inequality, polymatroid.
Keywords
Cite
@article{arxiv.1407.7405,
title = {Partition-Symmetrical Entropy Functions},
author = {Qi Chen and Raymond W. Yeung},
journal= {arXiv preprint arXiv:1407.7405},
year = {2016}
}
Comments
This paper is published in IEEE Transactions on Information Theory