Learning Bayesian Networks from Incomplete Databases
Artificial Intelligence
2013-02-08 v1 Machine Learning
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.
Cite
@article{arxiv.1302.1565,
title = {Learning Bayesian Networks from Incomplete Databases},
author = {Marco Ramoni and Paola Sebastiani},
journal= {arXiv preprint arXiv:1302.1565},
year = {2013}
}
Comments
Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)