Learning Bayesian Networks from Incomplete Databases

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.

Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)

Cited by in corpus (4)

• Searching for Bayesian Network Structures in the Space of Restricted Acyclic Partially Directed Graphs
• Evaluation of the Performance of the Markov Blanket Bayesian Classifier Algorithm
• On the Geometry of Bayesian Graphical Models with Hidden Variables
• Estimating Well-Performing Bayesian Networks using Bernoulli Mixtures