Abstract
This paper proposes a new data structure for representing potentials. Recursive probability trees are a generalization of probability trees. Both structures are able to represent context-specific independencies, but the new one is also able to hold a potential in a factorized way. This new structure can represent some kinds of potentials in a more efficient way than probability trees, and it can be the case that only recursive trees are able to represent certain factorizations. Basic operations for inference in Bayesian networks can be directly performed upon recursive probability trees.
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Cano, A., Gómez-Olmedo, M., Moral, S., Pérez-Ariza, C.B. (2010). Recursive Probability Trees for Bayesian Networks. In: Meseguer, P., Mandow, L., Gasca, R.M. (eds) Current Topics in Artificial Intelligence. CAEPIA 2009. Lecture Notes in Computer Science(), vol 5988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14264-2_25
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DOI: https://doi.org/10.1007/978-3-642-14264-2_25
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