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CORR
2011
Springer

Causal Dependence Tree Approximations of Joint Distributions for Multiple Random Processes

8 years 7 days ago
Causal Dependence Tree Approximations of Joint Distributions for Multiple Random Processes
—We investigate approximating joint distributions of random processes with causal dependence tree distributions. Such distributions are particularly useful in providing parsimonious representation when there exists causal dynamics among processes. By extending the results by Chow and Liu on dependence tree approximations, we show that the best causal dependence tree approximation is the one which maximizes the sum of directed informations on its edges, where best is defined in terms of minimizing the KL-divergence between the original and the approximate distribution. Moreover, we describe a low-complexity algorithm to efficiently pick this approximate distribution.
Christopher J. Quinn, Todd P. Coleman, Negar Kiyav
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2011
Where CORR
Authors Christopher J. Quinn, Todd P. Coleman, Negar Kiyavash
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