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Abstract: Most real-world data is stored in relational form. In contrast, most statistical learning methods, e.g., Bayesian network learning, work only with "flat" data representations, forcing us to convert our data into a form that loses much of the relational structure. The recently introduced framework of probabilistic relational models (PRMs) allow us to represent much richer dependency structures, involving multiple entities and the relations between them; they allow the properties of an... (Update)
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...data cases to be empty. This could be in our opinion in some cases too restrictive, e.g. in the case of dynamic Bayesian networks. FGKP99,GKTF00] adapted the Structural EM to learn the structure of probabilistic relational models. It applies the idea of the standard EM...
...Kersting and De Raedt [12] discuss a gradient based method to solve the same problem for Bayesian logic programs. Friedman et al. [6, 7] tackle the problem of learning the logical structure of rst order probabilistic models. They used StructuralEM for learning...
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4: Learning probabilistic relational models - Getoor, Friedman et al. - 1999
3: Probabilistic Horn abduction and Bayesian networks - Poole - 1993
3: Institute for Computer Science (context) - Kersting, De Raedt et al. - 2001
BibTeX entry: (Update)
L. Getoor, D. Koller, B. Taskar, and N. Friedman. Learning probabilistic relational models with structural uncertainty. In L. Getoor and D. Jensen, editors, Proceedings of the AAAI-2000 Workshop on Learning Statistical Models from Relational Data, 2000. http://citeseer.ist.psu.edu/getoor00learning.html More
@article{ getoor00learning,
author = "Lise Getoor",
title = "Learning Probabilistic Relational Models",
journal = "Lecture Notes in Computer Science",
volume = "1864",
pages = "322--??",
year = "2000",
url = "citeseer.ist.psu.edu/getoor00learning.html" }
Citations (may not include all citations):416 A Bayesian method for the induction of probabilistic network.. (context) - Cooper, Herskovits - 1992
219 A tutorial on learning with Bayesian networks - Heckerman - 1998
138 Probabilistic Horn abduction and Bayesian networks - Poole - 1993
103 Learning probabilistic relational models - Friedman, Getoor et al. - 1999
46 Probabilistic frame-based systems - Koller, Pfeffer - 1998
28 Answering queries from contextsensitive probabilistic knowle.. - Ngo, Haddawy - 1996
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