Abstract
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.
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Cooper, G.F. A Bayesian method for learning belief networks that contain hidden variables. J Intell Inf Syst 4, 71–88 (1995). https://doi.org/10.1007/BF00962823
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DOI: https://doi.org/10.1007/BF00962823