Combining bayesian networks with higher-order data representations

Authors:
Elias Gyftodimos;Peter A. Flach
Affiliations:
Computer Science Department, University of Bristol, UK;Computer Science Department, University of Bristol, UK
Venue:
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Year:
2005

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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.