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arXiv · 2504.21068

Polyhedral Aspects of Maxoids

Tobias Boege·Kamillo Ferry·Benjamin Hollering·Francesco Nowell
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Abstract

The conditional independence (CI) relation of a distribution in a max-linear Bayesian network depends on its weight matrix through the $C^\ast$-separation criterion. These CI~models, which we call maxoids, are compositional graphoids which are in general not representable by Gaussian random variables. We prove that every maxoid can be obtained from a transitively closed weighted DAG and show that the stratification of generic weight matrices by their maxoids yields a polyhedral~fan. We also use this connection to polyhedral geometry to develop an algorithm for solving the conditional independence implication problem for maxoids.

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Tobias Boege, Kamillo Ferry, Benjamin Hollering, Francesco Nowell. 2025-04-29. Polyhedral Aspects of Maxoids. https://arxiv.org/abs/2504.21068

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