License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagSemProc.05051.5
URN: urn:nbn:de:0030-drops-4139
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2006/413/
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Gyftodimos, Elias ;
Flach, Peter A.
Combining Bayesian Networks with Higher-Order Data Representations
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: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/opus/volltexte/2006/413},
URN = {urn:nbn:de:0030-drops-4139},
doi = {10.4230/DagSemProc.05051.5},
annote = {Keywords: Probabilistic reasoning, graphical models}
}
Keywords: |
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Probabilistic reasoning, graphical models |
Collection: |
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05051 - Probabilistic, Logical and Relational Learning - Towards a Synthesis |
Issue Date: |
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2006 |
Date of publication: |
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19.01.2006 |