Learning Recursive Probability Trees from Data

Abstract

Recursive Probability Trees offer a flexible framework for representing the probabilistic information in Probabilistic Graphical Models. This structure is able to provide a detailed representation of the distribution it encodes, by specifying most of the types of independencies that can be found in a probability distribution. Learning this structure involves the search for context-specific independencies along with factorisations within the available data. In this paper we develop the first approach at learning Recursive Probability Trees from data by extending an existent greedy methodology for retrieving small Recursive Probability Trees 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.