Learning the structure of Bayesian Networks: A quantitative assessment of the effect of different algorithmic schemes

TL;DR

This paper provides a comprehensive quantitative comparison of various algorithms for learning Bayesian Network structures from data, analyzing their performance across different data types and noise levels.

Contribution

It offers the first detailed assessment of the performance and characteristics of multiple heuristics for Bayesian Network structure learning.

Findings

Different algorithms vary significantly in accuracy and efficiency.

Performance depends on data type and noise level.

Certain scoring methods outperform others in specific scenarios.

Abstract

One of the most challenging tasks when adopting Bayesian Networks (BNs) is the one of learning their structure from data. This task is complicated by the huge search space of possible solutions, and by the fact that the problem is NP-hard. Hence, full enumeration of all the possible solutions is not always feasible and approximations are often required. However, to the best of our knowledge, a quantitative analysis of the performance and characteristics of the different heuristics to solve this problem has never been done before. For this reason, in this work, we provide a detailed comparison of many different state-of-the-arts methods for structural learning on simulated data considering both BNs with discrete and continuous variables, and with different rates of noise in the data. In particular, we investigate the performance of different widespread scores and algorithmic approaches proposed for the inference and the statistical pitfalls within them.