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Proceedings Paper

Using directed information for influence discovery in interconnected dynamical systems
Author(s): Arvind Rao; Alfred O. Hero; David J. States; James Douglas Engel
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Paper Abstract

Structure discovery in non-linear dynamical systems is an important and challenging problem that arises in various applications such as computational neuroscience, econometrics, and biological network discovery. Each of these systems have multiple interacting variables and the key problem is the inference of the underlying structure of the systems (which variables are connected to which others) based on the output observations (such as multiple time trajectories of the variables). Since such applications demand the inference of directed relationships among variables in these non-linear systems, current methods that have a linear assumption on structure or yield undirected variable dependencies are insufficient. Hence, in this work, we present a methodology for structure discovery using an information-theoretic metric called directed time information (DTI). Using both synthetic dynamical systems as well as true biological datasets (kidney development and T-cell data), we demonstrate the utility of DTI in such problems.

Paper Details

Date Published: 3 September 2008
PDF: 13 pages
Proc. SPIE 7074, Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII, 70740P (3 September 2008); doi: 10.1117/12.801360
Show Author Affiliations
Arvind Rao, Univ. of Michigan, Ann Arbor (United States)
Alfred O. Hero, Univ. of Michigan, Ann Arbor (United States)
David J. States, Univ. of Michigan, Ann Arbor (United States)
James Douglas Engel, Univ. of Michigan, Ann Arbor (United States)


Published in SPIE Proceedings Vol. 7074:
Advanced Signal Processing Algorithms, Architectures, and Implementations XVIII
Franklin T. Luk, Editor(s)

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