Abstract
We present an optimization to elimination tree inference in Bayesian networks through the use of unlabeled nodes, or nodes that are not labeled with a variable from the Bayesian network. Through the use of these unlabeled nodes, we are able to restructure these trees, and reduce the amount of computation performed during the inference process. Empirical tests show that the algorithm can reduce multiplications by up to 70%, and overall runtime by up to 50%.
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Grant, K., Scholten, K. (2010). On the Structure of Elimination Trees for Bayesian Network Inference. In: Sidorov, G., Hernández Aguirre, A., Reyes GarcÃa, C.A. (eds) Advances in Soft Computing. MICAI 2010. Lecture Notes in Computer Science(), vol 6438. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16773-7_18
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DOI: https://doi.org/10.1007/978-3-642-16773-7_18
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