Book Chapter10.1007/978-3-540-74695-9_17
Information theoretic derivations for causality detection: application to human gait
Gert Van Dijck,Jo Van Vaerenbergh,Marc M. Van Hulle +2 more
- 09 Sep 2007
- Vol. 17, pp 159-168
6
TL;DR: The conditional relative entropy criterion is compared with 3 well-established techniques for causality detection: 'Sims', 'Geweke-Meese-Dent' and 'Granger' and it is shown that the conditionalrelative entropy, as opposed to these 3 criteria, is sensitive to0.
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Abstract: As a causality criterion we propose the conditional relative entropy. The relationship with information theoretic functionals mutual information and entropy is established. The conditional relative entropy criterion is compared with 3 well-established techniques for causality detection: 'Sims', 'Geweke-Meese-Dent' and 'Granger'. It is shown that the conditional relative entropy, as opposed to these 3 criteria, is sensitive to0. non-linear causal relationships. All results are illustrated on real-world time series of human gait.
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Citations
The Assumption of Non-Gaussianity in Natural and Social Sciences and Its Influence on Detection of Causal Relationships
Kateřina Hlaváčková-Schindler
- 16 Aug 2012
TL;DR: The concept of causality has been traditionally studied in the field of metaphysics in philosophy as discussed by the authors, where the search for causes was a search for "first principles", which were meant to be explanatory.
The Assumption of Non-Gaussianity in Natural and Social Sciences and Its Influence on Detection of Causal Relationships
Katerina Hlavá ˇ cková-Schindler
- 01 Jan 2011
TL;DR: This paper investigated time series with a wider class probability distributions than Gaussian, the generalized Gaussian probability distributions, and set conditions on their parameters so that one can from their values decide whether the relationships between the involved time series are unidirectional causal or whether no causality is present.
1
information transfer and causal eect
Joseph T. Lizier,Mikhail Prokopenko +1 more
- 01 Jan 2010
TL;DR: Two existing measures, transfer entropy and information ow, which can be used separately to quantify information transfer and causal information respectively are discussed and applied to cellular automata on a local scale in space and time.
On Thermodynamic Interpretation of Transfer Entropy
TL;DR: A thermodynamic interpretation of transfer entropy near equilibrium is proposed, using a specialised Boltzmann’s principle, that relates conditional probabilities to the probabilities of the corresponding state transitions and shows that this difference, the local transfer entropy, is proportional to the external entropy production.
Differentiating information transfer and causal effect
TL;DR: It is shown that causal information flow is a primary tool to describe the causal structure of a system, while information transfer can then be used to describes the emergent computation on that causal structure.
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TL;DR: In this article, a statistical approach for identifying nonlinearity in time series is described, which first specifies some linear process as a null hypothesis, then generates surrogate data sets which are consistent with this null hypothesis and finally computes a discriminating statistic for the original and for each of the surrogate sets.
Testing for nonlinearity in time series: The method of surrogate data
James Theiler,B. Galdrikian,André Longtin,Stephen Eubank,J.D. Farmer +4 more
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TL;DR: A statistical approach for identifying nonlinearity in time series which is demonstrated for numerical data generated by known chaotic systems, and applied to a number of experimental time series, which arise in the measurement of superfluids, brain waves, and sunspots.