Recursive probability trees for Bayesian networks

Authors:
Andrés Cano;Manuel Gómez-Olmedo;Serafín Moral;Cora B. Pérez-Ariza
Affiliations:
Dept. Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Dept. Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Dept. Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Dept. Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Venue:
CAEPIA'09 Proceedings of the Current topics in artificial intelligence, and 13th conference on Spanish association for artificial intelligence
Year:
2009

Citing 8
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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.