Uncertainty in Artificial Intelligence
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Learning Bayesian Networks from Incomplete Databases
Marco Ramoni, Paola Sebastiani
Abstract:
Bayesian approaches to learn the graphical structure of Bayesian Belief Networks (BBNs) from databases share the assumption that the database is complete, that is, no entry is reported as unknown. Attempts to relax this assumption involve the use of expensive iterative methods to discriminate among different structures. This paper introduces a deterministic method to learn the graphical structure of a BBN from a possibly incomplete database. Experimental evaluations show a significant robustness of this method and a remarkable independence of its execution time from the number of missing data.
Keywords: Bayesian networks, Bayesian learning, model selection, missing data.
Pages: 401-408
PS Link: http://kmi.open.ac.uk/techreports/papers/kmi-tr-43.ps.gz
PDF Link: /papers/97/p401-ramoni.pdf
BibTex:
@INPROCEEDINGS{Ramoni97,
AUTHOR = "Marco Ramoni and Paola Sebastiani",
TITLE = "Learning Bayesian Networks from Incomplete Databases",
BOOKTITLE = "Proceedings of the Thirteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-97)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1997",
PAGES = "401--408"
}


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