A Bayesian Method for Learning Probabilistic 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 beliefnetwork structure that contains e. hidden (latent) variable. A hidden variable represents a postulated entity about which we have no data. For example, we may wish to postulate the existence of a hidden variable if we are looking for a hidden causal factor that influences the production of the data that we do observe.


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