Combining Bayesian Networks with Higher-Order Data Representations

Gyftodimos Elias, Peter Flach

Research output: Chapter in Book/Report/Conference proceedingConference 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 contributionCombining Bayesian Networks with Higher-Order Data Representations
Original languageEnglish
Title of host publicationProceedings of the 6th International Symposium on Intelligent Data Analysis (IDA'06)
Pages145-157
Publication statusPublished - 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

Fingerprint Dive into the research topics of 'Combining Bayesian Networks with Higher-Order Data Representations'. Together they form a unique fingerprint.

Cite this