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Structural Learning of Graphical Models and Its Applications to Traditional Chinese Medicine

  • Ke Deng
  • Delin Liu
  • Shan Gao
  • Zhi Geng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3614)

Abstract

Bayesian networks and undirected graphical models are often used to cope with uncertainty for complex systems with a large number of variables. They can be applied to discover causal relationships and associations between variables. In this paper, we present heuristic algorithms for structural learning of undirected graphical models from observed data. These algorithms are applied to traditional Chinese medicine.

Keywords

Mutual Information Traditional Chinese Medicine Akaike Information Criterion Bayesian Network Graphical Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ke Deng
    • 1
  • Delin Liu
    • 2
  • Shan Gao
    • 3
  • Zhi Geng
    • 1
  1. 1.School of Mathematical SciencesPeking UniversityBeijingChina
  2. 2.China Academy of Traditional Chinese MedicineBeijingChina
  3. 3.Peking Union Medical College HospitalBeijingChina

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