A method for learning belief networks that contain hidden variables

Cooper GF. A method for learning belief networks that contain hidden variables. Journal of Intelligent Information Systems 4 (1995) 1–18.

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

Publication Year: 
1995
Faculty Author: 
Publication Credits: 
Cooper GF
AttachmentSize
PDF icon Cooper.pdf998.71 KB
^