Characterising Probability Distributions via Entropies

TL;DR

This paper explores how entropy functions can be used to characterize correlations among sources, aiming to improve the understanding of network capacity regions with dependent sources.

Contribution

It introduces methods to utilize entropy functions for characterizing source dependencies, addressing a key challenge in network information theory.

Findings

Proposes a new approach to characterize source correlations using entropy functions

Provides insights into extending linear programming bounds for dependent sources

Enhances understanding of capacity regions in networks with correlated sources

Abstract

Characterising the capacity region for a network can be extremely difficult, especially when the sources are dependent. Most existing computable outer bounds are relaxations of the Linear Programming bound. One main challenge to extend linear program bounds to the case of correlated sources is the difficulty (or impossibility) of characterising arbitrary dependencies via entropy functions. This paper tackles the problem by addressing how to use entropy functions to characterise correlation among sources.