A comparison of Bayesian network learning algorithms from continuous data.

Learning a Bayesian network from data is an important problem in biomedicine for the automatic construction of decision support systems and inference of plausible causal relations. Most Bayesian network learning algorithms require discrete data; however discretization may impact the quality of the learned structure. In this project, we present a comparison of different approaches for learning from continuous data to identify the most promising one and to quantify the impact of discretization in Bayesian network learning.