DagSemProc.05051.5.pdf
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Combining Bayesian Networks with Higher-Order Data Representations
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Dagstuhl Seminar Proceedings, Volume 5051
Series: Dagstuhl Seminar Proceedings (DagSemProc) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2006-01-19
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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)
https://doi.org/10.4230/DagSemProc.05051.5
https://doi.org/10.4230/DagSemProc.05051.5
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.
Keywords
- Probabilistic reasoning
- graphical models
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