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Abstract: Learning Bayesian networks inductively in the presence of hidden variables is still an open problem. Even the simpler task of learning just the conditional probabilities on a Bayesian network with hidden variables is not completely solved. In this paper, we present an approach that learns the parameters of a Bayesian network composed of noisy-or and noisy-and nodes by using a gradient descent back-propagation approach similar to that used to train neural networks. For the task of causal... (Update)
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BibTeX entry: (Update)
@misc{ ramachandran-parameter,
author = "Sowmya Ramachandran and Raymond J. Mooney",
title = "Parameter Revision Techniques for Bayesian Networks with Hidden Variables:
An Experimental Comparison",
url = "citeseer.ist.psu.edu/ramachandran97parameter.html" }
Citations (may not include all citations):2528 Maximum likelihood from incomplete data via the EM algorithm (context) - Dempster, Laird et al. - 1977
1543 Probabilistic Reasoning in Intelligent Systems: Networks of .. (context) - Pearl - 1988
1359 Induction of decision trees (context) - Quinlan - 1986
493 Modeling by shortest data description (context) - Rissanen - 1978
351 Learning Bayesian networks: The combination of knowledge and.. - Heckerman, Geiger et al. - 1994
349 Knowledge acquisition via incremental conceptual clustering (context) - Fisher - 1987
303 Stochastic relaxation (context) - Geman, Geman - 1984
130 Refinement of approximate domain theories by knowledge-based.. - Towell, Shavlik et al. - 1990
121 An analysis of Bayesian classifiers - Langley, Iba et al. - 1992
118 Sequential updating of conditional probabilities on directed.. (context) - Spiegelhalter, Lauritzen - 1990
111 Connectionist learning of belief networks (context) - Neal - 1992
107 The EM algorithm for graphical association models with missi.. (context) - Lauritzen - 1995
101 Explorations in Parallel Distributed Processing: A Handbook .. (context) - McClelland, Rumelhart - 1988
89 Changing the rules: A comprehensive approach to theory refin.. (context) - Ourston, Mooney - 1990
84 Theory refinement on Bayesian networks - Buntine - 1991
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