AAAI Publications, Twenty-Second International FLAIRS Conference

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Constraint-based Approach to Discovery of Inter Module Dependencies in Modular Bayesian Networks
Patrick de Oude, Gregor Pavlin

Last modified: 2009-03-18


This paper introduces an information theoretic approach to verification of modular causal probabilistic models. We assume systems which are gradually extended by adding new functional modules, each having a limited domain knowledge captured by a local Bayesian network. Different modules originate from independent design processes. We assume that the local models are correct, which, however does not guarantee globally coherent inference in composed systems. The introduced method supports discovery of significant inter module dependencies which are ignored in the assembled Bayesian network.


Bayesian networks; structure learning; mutual information

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