(Enter summary)
Abstract: Interdependencies of stochastically interacting units are usually quanti
ed by the Kullback-Leibler divergence of a stationary joint probability
distribution on the set of all congurations from the corresponding factorized
distribution. This is a spatial approach which does not describe the
intrinsically temporal aspects of interaction. In the present paper the setting
is extended to a dynamical version where temporal interdependencies
are also captured by using information geometry of... (Update)
Context of citations to this paper: More
...it is referred to as (stochastic) interaction. In order to capture the intrinsically temporal aspects of interaction, I has been extended in [7] to the dynamical setting of Markov chains. In this case, the factorized or split Markov chains are the ones that can be composed by a...
.... to neural complexity [5,6] In order to capture intrinsically temporal aspects of dynamic interaction, I in (1) has been extended by Ay [2] to the dynamical setting of Markov chains, where it is referred to as (stochastic) interaction. The optimization of stochastic interaction...
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BibTeX entry: (Update)
Ay, N. (2001c) Information Geometry on Complexity and Stochastic Interaction. Submitted. 28 http://citeseer.ist.psu.edu/ay01information.html More
@misc{ ay-information,
author = "N. Ay",
title = "Information Geometry on Complexity and Stochastic Interaction",
text = "Ay, N. (2001c) Information Geometry on Complexity and Stochastic Interaction.
Submitted. 28",
url = "citeseer.ist.psu.edu/ay01information.html" }
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