Importance sampling in Bayesian networks using probability trees
Computational Statistics & Data Analysis
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Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Machine Learning
Nonuniform dynamic discretization in hybrid networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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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.