Inference of time-dependent causal influences in Networks

M. Killmann; L. Sommerlade; W. Mader; J. Timmer; B. Schelter

doi:10.1515/bmt-2012-4263

Inference of time-dependent causal influences in Networks

M. Killmann, L. Sommerlade, W. Mader, J. Timmer, B. Schelter

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

We address the challenge of detecting time-variant interactions in multivariate systems. Inferring Granger-causal interactions between processes promises to gain deeper insights into mechanisms underlying network phenomena, e.g., in the neurosciences. Renormalized partial directed coherence (rPDC) has been introduced as a means to investigate Granger causality in such multivariate systems. When using rPDC a major challenge is the reliable estimation of parameters in vector autoregressive processes. For time-varying connections a time-resolved estimation of the coefficients is mandatory. We show that the State Space Model in combination with the Kalman filter is a powerful tool for estimating time-variate AR process parameters.

Original language	English
Pages (from-to)	387-390
Number of pages	4
Journal	Biomedizinische Technik
Volume	57
Issue number	SUPPL. 1 TRACK-F
DOIs	https://doi.org/10.1515/bmt-2012-4263
Publication status	Published - 30 Aug 2012

Bibliographical note

Funding Information:
This work was supported by the German Science Foundation (Ti315/4-2) and the Excellence Initiative of the German Federal and State Governments. B.S. and L.S. are indebted to the Landesstiftung Baden-Württemberg for the financial support of this research project by the Eliteprogramme for Postdocs.

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AU - Schelter, B.

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Inference of time-dependent causal influences in Networks

Abstract

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