Published on Wed Feb 06 2013

Learning Bayesian Networks from Incomplete Databases

Marco Ramoni, Paola Sebastiani

Bayesian approaches to learn the graphical structure of Bayesian Belief Networks (BBNs) from databases share the assumption that the database is incomplete. Attempts to discriminate among BBN structures involve the use of expensive iterative methods. This paper introduces a deterministic method to learn BBN structure.

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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.

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