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Equivalence between minimal generative model graphs and directed information graphs

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3 Author(s)
Christopher J. Quinn ; Department of Electrical and Computer Engineering, University of Illinois, Urbana, 61801, USA ; Negar Kiyavash ; Todd P. Coleman

We propose a new type of probabilistic graphical model, based on directed information, to represent the causal dynamics between processes in a stochastic system. We show the practical significance of such graphs by proving their equivalence to generative model graphs which succinctly summarize interdependencies for causal dynamical systems under mild assumptions. This equivalence means that directed information graphs may be used for causal inference and learning tasks in the same manner Bayesian networks are used for correlative statistical inference and learning.

Published in:

Information Theory Proceedings (ISIT), 2011 IEEE International Symposium on

Date of Conference:

July 31 2011-Aug. 5 2011