Entropic Inequalities and Marginal Problems
Tobias Fritz, Rafael Chaves

TL;DR
This paper explores how Shannon entropic inequalities can serve as necessary conditions for the existence of joint distributions in marginal problems, providing computational tools and analytic solutions, especially for cycle graphs.
Contribution
It introduces a framework linking entropic inequalities to marginal problems, offers a software tool for their computation, and provides a complete analytic classification for cycle graphs.
Findings
Entropic inequalities are necessary conditions for joint distributions.
Complete analytic solution for cycle graph marginal problems.
Non-Shannon inequalities are relevant for continuous variables.
Abstract
A marginal problem asks whether a given family of marginal distributions for some set of random variables arises from some joint distribution of these variables. Here we point out that the existence of such a joint distribution imposes non-trivial conditions already on the level of Shannon entropies of the given marginals. These entropic inequalities are necessary (but not sufficient) criteria for the existence of a joint distribution. For every marginal problem, a list of such Shannon-type entropic inequalities can be calculated by Fourier-Motzkin elimination, and we offer a software interface to a Fourier-Motzkin solver for doing so. For the case that the hypergraph of given marginals is a cycle graph, we provide a complete analytic solution to the problem of classifying all relevant entropic inequalities, and use this result to bound the decay of correlations in stochastic processes.…
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