A Bayesian method for learning belief networks that contain hidden variables

Cooper GF. A Bayesian method for learning belief networks that contain hidden variables. Proceedings of the Workshop on Knowledge Discovery in Databases (1993) 112–124

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 handing hidden variables in belief networks.
Keywords: probabilistic networks, Bayesian belief networks, hidden variables, machine learning, inductio

Publication Year: 
1993
Faculty Author: 
Publication Credits: 
Cooper GF
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