A Bayesian method for learning belief networks that contain hidden variables

摘要

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.