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Combining Bayesian Networks with Higher-Order Data Representations

Elias Gyftodimos, Peter A. Flach, Combining Bayesian Networks with Higher-Order Data Representations. Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA'06). ISBN 3-540-28795-7, pp. 145–157. September 2005. PDF, 309 Kbytes.

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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