Uncertainty in Artificial Intelligence
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Bayesian network learning with cutting planes
James Cussens
Abstract:
The problem of learning the structure of Bayesian networks from complete discrete data with a limit on parent set size is considered. Learning is cast explicitly as an optimisation problem where the goal is to find a BN structure which maximises log marginal likelihood (BDe score). Integer programming, specifically the SCIP framework, is used to solve this optimisation problem. Acyclicity constraints are added to the integer program (IP) during solving in the form of cutting planes. Finding good cutting planes is the key to the success of the approach -the search for such cutting planes is effected using a sub-IP. Results show that this is a particularly fast method for exact BN learning.
Keywords:
Pages: 153-160
PS Link:
PDF Link: /papers/11/p153-cussens.pdf
BibTex:
@INPROCEEDINGS{Cussens11,
AUTHOR = "James Cussens ",
TITLE = "Bayesian network learning with cutting planes",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "153--160"
}


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