Journal of Intelligent Information Systems

, Volume 4, Issue 1, pp 71–88 | Cite as

A Bayesian method for learning belief networks that contain hidden variables

  • Gregory F. Cooper


This paper presents a Bayesian method for computing the probability of a Bayesian belief-network structure from a database. In particular, the paper focuses on computing the probability of a belief-network structure that contains a hidden (latent) variable. A hidden variable represents a postulated entity that has not been directly measured. After reviewing related techniques, which previously were reported, this paper presents a new, more efficient method for handling hidden variables in belief networks.


probabilistic networks Bayesian belief networks hidden variables machine learning induction 


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Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Gregory F. Cooper
    • 1
  1. 1.Section of Medical InformaticsUniversity of PittsburghPittsburgh

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