Reasoning methods in general and stuctured Bayesian networks

Authors

  • Mieczysław Kłopotek

Abstract

Bayesian networks have many practical applications due to their capability to represent joint probability distribution in many variables in a compact way. Though there exist many algorithms for learning Bayesian networks from data, they are not satisfactory because the learned networks usually are not suitable for reasoning. So far only a restricted class of very simple Bayesian networks: trees and poly-trees are directly applicable in reasoning. This paper defines and explores a new class of networks: the Structured Bayesian Networks. Two methods of reasoning are outlined for this type of networks. Possible methods of learning from data are indicated. Similarity to hiearachical networks is pointed at.

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Published

2003-06-15

How to Cite

Kłopotek, M. (2003). Reasoning methods in general and stuctured Bayesian networks. Studia Informatica. System and Information Technology, 1(1), 5–25. Retrieved from https://czasopisma.uph.edu.pl/studiainformatica/article/view/2910