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Discriminating between causal structures in Bayesian Networks given partial observations

Philipp MoritzJörg ReichardtNihat Ay — 2014

Kybernetika

Given a fixed dependency graph G that describes a Bayesian network of binary variables X 1 , , X n , our main result is a tight bound on the mutual information I c ( Y 1 , , Y k ) = j = 1 k H ( Y j ) / c - H ( Y 1 , , Y k ) of an observed subset Y 1 , , Y k of the variables X 1 , , X n . Our bound depends on certain quantities that can be computed from the connective structure of the nodes in G . Thus it allows to discriminate between different dependency graphs for a probability distribution, as we show from numerical experiments.

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