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2010

Approximating discrete probability distributions with causal dependence trees

9 years 5 months ago
Approximating discrete probability distributions with causal dependence trees
Abstract--Chow and Liu considered the problem of approximating discrete joint distributions with dependence tree distributions where the goodness of the approximations were measured in terms of KL distance. They (i) demonstrated that the minimum divergence approximation was the tree with maximum sum of mutual informations, and (ii) specified a low-complexity minimum-weight spanning tree algorithm to find the optimal tree. In this paper, we 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. We (i) demonstrate that the minimum divergence approximation is the directed tree with maximum sum of directed informations, and (ii) specify a low-complexity minimum weight directed spanning tree, or arborescence, algorithm to find the optimal tree.
Christopher J. Quinn, Todd P. Coleman, Negar Kiyav
Added 13 Feb 2011
Updated 13 Feb 2011
Type Journal
Year 2010
Where ISITA
Authors Christopher J. Quinn, Todd P. Coleman, Negar Kiyavash
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