Location of Repository

A Comparison of Bayesian Network Learning Algorithms from Continuous Data

By Lawrence D Fu and Ioannis Tsamardinos

Abstract

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

Topics: Article
Publisher: American Medical Informatics Association
OAI identifier: oai:pubmedcentral.nih.gov:1560522
Provided by: PubMed Central
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.pubmedcentral.nih.g... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.