A Bayesian method for the induction of probabilistic networks from data

Cooper GF, Herskovits EH. A Bayesian method for the induction of probabilistic networks from data. Machine Learning 9 (1992) 309–347.

 This paper presents a Bayesianm ethod for constructing probabilistic networks from databases. In par- ticular, we focus on constructing Bayesianb elief networks. Potential applicationsi nclude computer-assistedh ypoth- esis testing, automated scientific discovery,a nd automated construction of probabifistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probahilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a databaseo f cases. Finally,w e relate the methods in this paper to previous work, and we discuss open problems.
Keywords. probabilistic networks, Bayesian belief networks, machine learning, induction

Publication Year: 
1992
Faculty Author: 
Publication Credits: 
Cooper GF, Herskovits EH.
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