ITEM METADATA RECORD
Title: Information Theoretic Derivations for Causality Detection: Application to Human Gait
Authors: Van Dijck, Gert
Van Vaerenbergh, Jozef
Van Hulle, Marc #
Issue Date: Sep-2007
Publisher: Springer
Host Document: Proceedings of the 17th International Conference on Artificial Neural Networks vol:17 pages:159-168
Conference: International Conference on Artificial Neural Networks edition:17 location:Porto, Portugal date:9-13 September
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 to non-linear causal relationships. All results are illustrated on real-world time series of human gait.
ISBN: 978-3-540-74693-5
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Laboratory for Neuro- and Psychofysiology
Research Group Neurophysiology
# (joint) last author

Files in This Item:
File Description Status SizeFormat
id1419_10.pdfMain article Published 316KbAdobe PDFView/Open Request a copy

These files are only available to some KU Leuven Association staff members

 




All items in Lirias are protected by copyright, with all rights reserved.