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Approximating Discrete Probability Distributions with Causal Dependence Trees

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Executive Summary

Chow and Liu considered the problem of approximating discrete joint distributions with dependence tree distributions where the goodness of the approximations was measured in terms of KL distance. They demonstrated that the minimum divergence approximation was the tree with maximum sum of mutual informations, and specified a low-complexity minimum-weight spanning tree algorithm to find the optimal tree. In this paper, the authors consider an analogous problem of approximating the joint distribution on discrete random processes with causal, directed, dependence trees, where the approximation is again measured in terms of KL distance.

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