An Extended Approach to Learning Recursive Probability Trees from Data

Abstract

Rlatecursive probability trees (RPTs) offer a flexible framework for representing the probabilistic information in probabilistic graphical models. This structure is able to provide a compact representation of the distribution it encodes by specifying most of the types of independencies that can be found in a probability distribution. The real benefit of this representation heavily depends on the ability of learning such independencies from data. In this paper, we expand our approach at learning RPTs from data by extending an existing greedy methodology for retrieving small RPTs from probabilistic potentials. We test the performance of the algorithm by learning from different databases, both real and handcrafted, and we compare the performance for different databases sizes. C 2015 Wiley Periodicals, Inc.