Information transfer in continuous processes

By A. Kaiser, Thomas Schreiber. 2002

342 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/feca3a7191ab85070883b7c102bccc533a0f44ea

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Citations in the corpus

Transfer entropy—a model-free measure of effective connectivity for the neurosciences (2010, methodology): “However, other nonparametric techniques such as kernel or nearestneighbor estimators have been shown to be more data efficient and accurate while avoiding certain arbitrariness stemming from binning (Victor 2002; Kaiser and Schreiber 2002).”
Exploring Causal Relationships in Visual Object Tracking (2015, background): “TE has since found its place in many areas, including again neurosciences [36], chemistry [3] and others [23].”
Testing for Causality in Continuous Time Bayesian Network Models of High-Frequency Data (2016, background): “Kaiser and Schreiber (2002) consider the directed information for processes in discrete time and takes the limit, thereby forming a continuum of instances.”
Wiener-Granger Causality: A well established methodology (2011, neutral): “On the other hand, the estimation of TE by state-space partitioning is problematic, especially in multivariate situations (Kaiser and Schreiber, 2002).”
Nonlinear multivariate analysis of neurophysiological signals. (2005, background): “(17) and its equivalent for Y, (hY), it is possible to calculate the transfer entropy TX→Y, which is a measure of how the transition probabilities of X (i.e., its dynamics) influence those of Y (Kaiser and Schreiber, 2002; Kraskov, 2004; Schreiber, 2000).”
A method for detecting damage-induced nonlinearities in structures using information theory (2006, methodology): “[20].”
The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. (2014, background): “6) and an information-theoretic interpretation as a transfer entropy (Schreiber, 2000; Kaiser and Schreiber, 2002; Barnett et al., 2009; Barnett and Bossomaier, 2013).”
JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems (2014, background): “, transfer entropy as per (Kaiser and Schreiber, 2002)).”
Neural interactions in the human frontal cortex dissociate reward and punishment learning (2024, methodology): “The TE can be formulated in terms of conditional mutual information (Schreiber, 2000; Kaiser and Schreiber, 2002): Here, we computed the TE on the gamma activity time courses of pairs of iEEG contacts.”