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Inferring the directionality of coupling with conditional mutual information
1Institute of Computer Science, Academy of Sciences of the Czech Republic, Praha, Czech Republic. vejmelka@cs.cas.cz
This study introduces a nonparametric method to determine coupling directionality in complex systems by estimating information theory functionals. This approach helps understand drive-response relationships and is validated using chaotic oscillators.
Area of Science:
- Complex Systems Science
- Information Theory
- Nonparametric Statistics
Background:
- Understanding drive-response relationships is crucial for analyzing complex systems.
- Determining the directionality of coupling between system components is a key challenge.
Purpose of the Study:
- To present a novel nonparametric method for detecting coupling directionality.
- To evaluate the performance of different conditional mutual information estimators.
- To investigate the influence of phase extraction and frequency ratio in chaotic systems.
Main Methods:
- Estimation of information theoretic functionals, specifically conditional mutual information.
- Analysis of estimator behavior using a linear model with analytical conditional mutual information.
- Numerical experiments on chaotic oscillators to assess coupling directionality detection.
Main Results:
- The proposed nonparametric method effectively detects coupling directionality.
- The study details the behavior of various conditional mutual information estimators.
- Phase extraction methods and relative frequency ratios impact the accuracy of directionality detection in chaotic systems.
Conclusions:
- The nonparametric approach based on information theory provides a robust tool for uncovering coupling directionality.
- The findings offer insights into selecting appropriate methods for analyzing complex system dynamics.
- This research contributes to a deeper understanding of causal relationships in dynamical systems.
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