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Physical Review Letters, 2008
We propose to estimate transfer entropy using a technique of symbolization. We demonstrate numerically that symbolic transfer entropy is a robust and computationally fast method to quantify the dominating direction of information flow between time series from structurally identical and nonidentical coupled systems.
Matthäus, Staniek, Klaus, Lehnertz
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We propose to estimate transfer entropy using a technique of symbolization. We demonstrate numerically that symbolic transfer entropy is a robust and computationally fast method to quantify the dominating direction of information flow between time series from structurally identical and nonidentical coupled systems.
Matthäus, Staniek, Klaus, Lehnertz
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Transfer entropy on symbolic recurrences
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2019Recurrence quantification analysis offers a powerful framework to investigate complexity in dynamical systems. While several studies have demonstrated the possibility of multivariate recurrence quantification analysis, information-theoretic tools for the discovery of causal links remain elusive.
Maurizio Porfiri, Manuel Ruiz Marín
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Accelerating transfer entropy computation
2014 International Conference on Field-Programmable Technology (FPT), 2014Transfer entropy is a measure of information transfer between two time series. It is an asymmetric measure based on entropy change which only takes into account the statistical dependency originating in the source series, but excludes dependency on a common external factor.
Shengjia Shao +3 more
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Entropy barriers to proton transfer
Journal of the American Chemical Society, 1991Proton transfer between sterically hindered pyridines and amines proceeds through locked-rotor, low-entropy intermediates. The reactions exhibit slow kinetics (efficiencies of 0.1-0.0001) and large negative temperature coefficients (up to k=CT − 8 .7 ).
Meotner, M, Smith, SC
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Abnormal Symbolic Transfer Entropy in Depression
The Fifth International Conference on Biological Information and Biomedical Engineering, 2021Depression is a mental illness and considered the main cause of disability worldwide. Further study is still needed to enhance the accuracy of depression detection. The aim of this study was to explore the potential EEG biomarker for cortical dysfunction to help the diagnosis with depression clinically. In this study, symbolic transfer entropy (STE) of
Yangting Zhang, Yuxi Luo
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Spurious causalities with transfer entropy
Physical Review E, 2013Transfer entropy (TE) seems currently to be the most widely used tool to characterize causal influences in ensembles of complex systems from observed time series. In particular, in an elemental case of two systems, nonzero TEs in both directions are usually interpreted as a sign of a bidirectional coupling.
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Multiscale transfer entropy: Measuring information transfer on multiple time scales
Communications in Nonlinear Science and Numerical Simulation, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Xiaojun Zhao +2 more
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Transfer Entropy in Neuroscience
2014Information transfer is a key component of information processing, next to information storage and modification. Information transfer can be measured by a variety of directed informationmeasures of which transfer entropy is themost popular, andmost principled one. This chapter presents the basic concepts behind transfer entropy in an intuitive fashion,
Michael Wibral +2 more
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Polarization entropy transfer and relative polarization entropy
Optics Communications, 1996Abstract The concept of polarization entropy is extended to include its transfer by an optical system (or scattering medium) characterized by a Jones matrix (or equivalently, a Mueller-Jones matrix) and also by a general Mueller matrix. It is shown that physical realizability for a deterministic, passive medium governed by a Jones matrix implies ...
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Localization and regularization of normalized transfer entropy
Neurocomputing, 2014To find hidden structures of a data set, it is important to understand the relationship between variables such as genes or neurons. As a measure of such relationship, causality is to find directed relations between the variables, which can reveal more of the structures than undirected relations.
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