Learning recursive probability trees from probabilistic potentials
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Cano, Andrés; Gómez Olmedo, Manuel; Moral, Serafín; Pérez-Ariza, Cora B.; Salmerón Cerdán, AntonioDate
2010Abstract
A recursive probability tree (RPT) is an incipient data structure for representing the distributions in a probabilistic graphical model. RPTs capture most of the types of independencies found in a probability distribution. The explicit representation of these features using RPTs simplifies computations during inference. This paper describes a learning algorithm that builds a RPT from a probability distribution. Experiments prove that this algorithm generates a good approximation of the original distribution, thus making available all the advantages provided by RPTs