Minimal Characterization of Shannon-Type Inequalities Under Functional Dependence and Full Conditional Independence Structures

The minimal set of Shannon-type inequalities (or elemental inequalities) plays a central role in efficiently determining whether a given inequality is in fact Shannon-type or not and in computing the linear programming bound for network coding capacity. In many cases, random variables under consideration are subject to additional constraints, such as functional dependence and conditional independence constraints. For example, functional dependence constraints are common in many communication problems due to deterministic encoding and decoding constraints. In other situations, the variables involved may form a Markov chain or in general a Markov random field, leading to conditional independence constraint. Subject to additional constraints, the challenge is how to identify the non-redundant inequalities. While one can always numerically determine the non-redundant inequalities (subject to additional linear equality constraints), it will be instrumental and also important if the non-redundant inequalities can be listed explicitly. In this paper, we show that this is achievable under the functional dependence and full conditional independence constraints.