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Combining Bayesian Networks with Higher-Order Data Representations Gyftodimos, Elias Flach, Peter A. Probabilistic reasoning graphical models 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Elias Gyftodimos and Peter A. Flach 2006 Is Part Of Dagstuhl Seminar Proceedings, Volume 5051, Probabilistic, Logical and Relational Learning - Towards a Synthesis (2006) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.05051.5 urn:nbn:de:0030-drops-4139 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05051.5 eng https://creativecommons.org/licenses/by/4.0/legalcode