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<p><b>Abstract</b>—This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples.</p>
Bayesian networks, graphical models, hidden variables, learning, learning structure, probabilistic networks, knowledge discovery.

W. Buntine, "A Guide to the Literature on Learning Probabilistic Networks from Data," in IEEE Transactions on Knowledge & Data Engineering, vol. 8, no. , pp. 195-210, 1996.
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