Combining Bayesian Networks with Higher-Order Data Representations

Elias Gyftodimos, Peter A. Flach

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

2 Citations (Scopus)

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 publicationProbabilistic, Logical and Relationial Learning - Towards a Synthesis
PublisherDagstuhl Seminar Proceedings 05051:
Publication statusPublished - 2005

Bibliographical note

Other page information: -
Conference Proceedings/Title of Journal: Probabilistic, Logical and Relationial Learning - Towards a Synthesis
Other identifier: 2000554

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