Learning Bayesian networks from incomplete databases

Authors:
Marco Ramoni;Paola Sebastiani
Affiliations:
Knowledge Media Institute, The Open University;Department of Actuarial Science and Statistics, City University
Venue:
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Year:
1997

Citing 6
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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.