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Abstract: In recent years there has been a flurry of works on learning probabilistic belief networks. Current state of the art methods have been shown to be successful for two learning scenarios: learning both network structure and parameters from complete data, and learning parameters for a fixed network from incomplete data---that is, in the presence of missing values or hidden variables. However, no method has yet been demonstrated to effectively learn network structure from incomplete data. In this... (Update)
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...and cannot be evaluated directly. The structural EM algorithm evaluates the expected score of a network based on some initial network [11], 12] Q(S ; jS; E S; fScore(S; D)g (69) The expectation is taken with respect to P (Xh jD; S; The computation of the...
...vectors are not important. Second, our search strategy is different form the one of [5] We use the structural EM algorithm proposed in [6] to find the optimal DBN. In the next section, we define the class of structures (i.e. dependencies) we are interested in. In section 3, we...
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BibTeX entry: (Update)
Friedman, N. (1997), Learning belief networks in the presence of missing values and hidden variables, in D. Fisher, ed., `Proceedings of the Fourteenth International Conference on Machine Learning', Morgan Kaufmann, San Francisco, CA, pp. 125-- 133. http://citeseer.ist.psu.edu/friedman97learning.html More
@inproceedings{ friedman97learning,
author = "Nir Friedman",
title = "Learning belief networks in the presence of missing values and hidden variables",
booktitle = "Proc. 14th International Conference on Machine Learning",
publisher = "Morgan Kaufmann",
pages = "125--133",
year = "1997",
url = "citeseer.ist.psu.edu/friedman97learning.html" }
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