Learning Bayesian Networks from Incomplete Databases

TL;DR

This paper presents a deterministic method for learning Bayesian network structures from incomplete databases, demonstrating robustness and efficiency regardless of missing data proportion.

Contribution

It introduces a novel deterministic approach that effectively handles incomplete data without relying on iterative methods, improving robustness and computational efficiency.

Findings

Method is robust to missing data

Execution time is independent of missing data amount

Outperforms iterative methods in efficiency

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.