Binary Probability Trees for Bayesian Networks Inference

Abstract

The present paper introduces a new kind of representation for the potentials in a Bayesian network: Binary Probability Trees. They allow to represent finer grain context-specific independences than those which can be encoded with probability trees. This enhanced capability leads to more efficient inference algorithms in some types of Bayesian networks. The paper explains how to build a binary tree from a given potential with a similar procedure to the one employed for probability trees. It also offers a way of pruning a binary tree if exact inference cannot be performed with exact trees, and provides detailed algorithms for performing directly with binary trees the basic operations on potentials (restriction, combination and marginalization). Finally, some experiments are shown that use binary trees with the variable elimination algorithm to compare the performance with standard probability trees.