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URN: urn:nbn:de:0030-drops-4139
URL: http://drops.dagstuhl.de/opus/volltexte/2006/413/
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Gyftodimos, Elias ; Flach, Peter A.

Combining Bayesian Networks with Higher-Order Data Representations

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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.

BibTeX - Entry

@InProceedings{gyftodimos_et_al:DSP:2006:413,
  author =	{Elias Gyftodimos and Peter A. Flach},
  title =	{Combining Bayesian Networks with Higher-Order Data Representations},
  booktitle =	{Probabilistic, Logical and Relational Learning - Towards a Synthesis},
  year =	{2006},
  editor =	{Luc De Raedt and Thomas Dietterich and Lise Getoor  and Stephen H. Muggleton},
  number =	{05051},
  series =	{Dagstuhl Seminar Proceedings},
  ISSN =	{1862-4405},
  publisher =	{Internationales Begegnungs- und Forschungszentrum f{\"u}r Informatik (IBFI), Schloss Dagstuhl, Germany},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2006/413},
  annote =	{Keywords: Probabilistic reasoning, graphical models}
}

Keywords: Probabilistic reasoning, graphical models
Seminar: 05051 - Probabilistic, Logical and Relational Learning - Towards a Synthesis
Issue Date: 2006
Date of publication: 19.01.2006


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