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Combining Bayesian Networks with Higher-Order Data Representations

Authors: Elias Gyftodimos and Peter A. Flach

Published in: Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006)

Abstract

This paper introduces Higher-Order Bayesian Networks, a probabilistic reasoning formalism which combines the efficient reasoning mechanisms of Bayesian Networks with the expressive power of higher-order logics. We discuss how the proposed graphical model is used in order to define a probability distribution semantics over particular families of higher-order terms. We give an example of the application of our method on the Mutagenesis domain, a popular dataset from the Inductive Logic Programming community, showing how we employ probabilistic inference and model learning for the construction of a probabilistic classifier based on Higher-Order Bayesian Networks.

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Elias Gyftodimos and Peter A. Flach. Combining Bayesian Networks with Higher-Order Data Representations. In Probabilistic, Logical and Relational Learning - Towards a Synthesis. Dagstuhl Seminar Proceedings, Volume 5051, pp. 1-10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{gyftodimos_et_al:DagSemProc.05051.5,
  author =	{Gyftodimos, Elias and Flach, Peter A.},
  title =	{{Combining Bayesian Networks with Higher-Order Data Representations}},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  pages =	{1--10},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5051},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor and Stephen H. Muggleton},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.5},
  URN =		{urn:nbn:de:0030-drops-4139},
  doi =		{10.4230/DagSemProc.05051.5},
  annote =	{Keywords: Probabilistic reasoning, graphical models}
}

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Combining Bayesian Networks with Higher-Order Data Representations

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