Constraint-based approach to discovery of inter module dependencies in modular Bayesian networks

Authors
Publication date 2009
Host editors
  • H.C. Lane
  • H.W. Guesgen
Book title Proceedings of the Twenty-Second International Florida Artificial Intelligence Research Society Conference: 19-21 May 2009, Sanibel Island, Florida, USA
ISBN
  • 9781577354192
Event Twenty-Second International Florida Artificial Intelligence Research Society Conference (FLAIRS-22), Sanibel Island, FL, USA
Pages (from-to) 529-534
Publisher Menlo Park, CA: AAAI Press
Organisations
  • Faculty of Science (FNWI) - Informatics Institute (IVI)
Abstract 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.
Document type Conference contribution
Published at http://www.aaai.org/ocs/index.php/FLAIRS/2009/paper/view/97
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