Abstract
Semantics in Bayesian network inference has received an increasing level of interest in recent years. This paper considers the use of semantics in Bayesian network inference using Lazy Propagation. In particular, we describe how the semantics of potentials created during belief update can be determined using the Semantics in Inference algorithm. This includes a description of the necessary properties of Semantics in Inference to make the task feasible to be performed as part of belief update. The paper also reports on the results of an experimental analysis designed to determine the average number of potentials and distributions created during belief update on a set of real-world Bayesian networks.
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Madsen, A.L., Butz, C.J. (2015). Exploiting Semantics in Bayesian Network Inference Using Lazy Propagation. In: Barbosa, D., Milios, E. (eds) Advances in Artificial Intelligence. Canadian AI 2015. Lecture Notes in Computer Science(), vol 9091. Springer, Cham. https://doi.org/10.1007/978-3-319-18356-5_1
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DOI: https://doi.org/10.1007/978-3-319-18356-5_1
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