Combining Bayesian Networks with Higher-Order Data Representations

2006/01/01 by Elias Gyftodimos, Gyftodimos, Elias, Peter Flach +1
Computer Science · #Bayesian Modeling and Causal Inference #Logic, Reasoning, and Knowledge #Probabilistic reasoning #Semantic Web and Ontologies #graphical models

paper · doi:10.4230/dagsemproc.05051.5

openalex publication_date 2006/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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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