Combining Bayesian Networks with Higher-Order Data Representations

Gyftodimos Elias; Peter Flach

Combining Bayesian Networks with Higher-Order Data Representations

Gyftodimos Elias, Peter Flach

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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.

Translated title of the contribution	Combining Bayesian Networks with Higher-Order Data Representations
Original language	English
Title of host publication	Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA'06)
Pages	145-157
Publication status	Published - 2005

Bibliographical note

ISBN: 3540287957
Publisher: Springer-Verlag
Name and Venue of Conference: Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA'06)
Other identifier: 2000553

Access to Document

http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=2000553

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Cite this

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title = "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. ",

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AB - 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.

M3 - Conference Contribution (Conference Proceeding)

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BT - Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA'06)

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