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MiS Preprint

Properties of Unique Information

Johannes Rauh, Maik Schünemann and Jürgen Jost


We study the unique information $UI(T:X\setminus Y)$ defined by Bertschinger et al. (2014) within the framework of information decompositions. In particular, we study uniqueness and support of the solutions to the convex optimization problem underlying the definition of $UI$. We identify sufficient conditions for non-uniqueness of solutions with full support in terms of conditional independence constraints and in terms of the cardinalities of $T$, $X$ and $Y$. Our results are based on a reformulation of the first order conditions on the objective function as rank constraints on a matrix of conditional probabilities. These results help to speed up the computation of $UI(T:X\setminus Y)$, most notably when $T$ is binary. Optima in the relative interior of the optimization domain are solutions of linear equations if $T$ is binary. In the all binary case, we obtain a complete picture of where the optimizing probability distributions lie.

Jan 6, 2020
Jan 6, 2020
MSC Codes:
94A15, 94A17
information decomposition, Unique Information

Related publications

2021 Journal Open Access
Johannes Rauh, Maik Schünemann and Jürgen Jost

Properties of unique information

In: Kybernetika, 57 (2021) 3, pp. 383-403