41,253 research outputs found
Editorial Comment on the Special Issue of "Information in Dynamical Systems and Complex Systems"
This special issue collects contributions from the participants of the
"Information in Dynamical Systems and Complex Systems" workshop, which cover a
wide range of important problems and new approaches that lie in the
intersection of information theory and dynamical systems. The contributions
include theoretical characterization and understanding of the different types
of information flow and causality in general stochastic processes, inference
and identification of coupling structure and parameters of system dynamics,
rigorous coarse-grain modeling of network dynamical systems, and exact
statistical testing of fundamental information-theoretic quantities such as the
mutual information. The collective efforts reported herein reflect a modern
perspective of the intimate connection between dynamical systems and
information flow, leading to the promise of better understanding and modeling
of natural complex systems and better/optimal design of engineering systems
Revealing Relationships among Relevant Climate Variables with Information Theory
A primary objective of the NASA Earth-Sun Exploration Technology Office is to
understand the observed Earth climate variability, thus enabling the
determination and prediction of the climate's response to both natural and
human-induced forcing. We are currently developing a suite of computational
tools that will allow researchers to calculate, from data, a variety of
information-theoretic quantities such as mutual information, which can be used
to identify relationships among climate variables, and transfer entropy, which
indicates the possibility of causal interactions. Our tools estimate these
quantities along with their associated error bars, the latter of which is
critical for describing the degree of uncertainty in the estimates. This work
is based upon optimal binning techniques that we have developed for
piecewise-constant, histogram-style models of the underlying density functions.
Two useful side benefits have already been discovered. The first allows a
researcher to determine whether there exist sufficient data to estimate the
underlying probability density. The second permits one to determine an
acceptable degree of round-off when compressing data for efficient transfer and
storage. We also demonstrate how mutual information and transfer entropy can be
applied so as to allow researchers not only to identify relations among climate
variables, but also to characterize and quantify their possible causal
interactions.Comment: 14 pages, 5 figures, Proceedings of the Earth-Sun System Technology
Conference (ESTC 2005), Adelphi, M
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