Learning recursive probability trees from probabilistic potentials

Authors:
AndréS Cano;Manuel GóMez-Olmedo;SerafíN Moral;Cora B. PéRez-Ariza;Antonio SalmeróN
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Statistics and Applied Mathematics, University of Almerı´a, Almeria, Spain
Venue:
International Journal of Approximate Reasoning
Year:
2012

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Abstract

A Recursive Probability Tree (RPT) is a data structure for representing the potentials involved in Probabilistic Graphical Models (PGMs). This structure is developed with the aim of capturing some types of independencies that cannot be represented with previous structures. This capability leads to improvements in memory space and computation time during inference. This paper describes a learning algorithm for building RPTs from probability distributions. The experimental analysis shows the proper behavior of the algorithm: it produces RPTs encoding good approximations of the original probability distributions.