Special Issue "Transfer Entropy"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: closed (31 December 2014)
Dr. Deniz Gencaga
Carnegie Mellon University Pittsburgh, PA, USA
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Interests: bayesian data analysis; statistical signal processing; machine learning (for big data); information theory; source separation; computational mathematics and statistics; autonomous and intelligent systems; data mining and knowledge discovery; remote sensing; climatology; astronomy; systems biology; smart grid Contribution: Special Issue: Transfer Entropy
In many research fields, we need to analyze the causal interactions among the variables of a complex system, to better understand the physical behavior of it. While linear techniques, such as correlation, are widely used to identify and characterize these relationships, more advanced information-based techniques, such as mutual information and transfer entropy, have proven to be superior. Transfer entropy has been used to analyze the causal relationships between subsystem variables from data. The fact that it is non-symmetric enables one to infer the direction of information flow. Granger causality, which generally relies on autoregression to assess interactions, has been shown to be equivalent to transfer entropy in the case of Gaussian variables.
In this special issue, we would like to collect papers focusing on both the theory and applications of Transfer Entropy. The application areas are diverse and include neuroscience, systems biology, bioinformatics, environmental sciences, climatology, engineering, finance, astronomy, Earth and space sciences, and astronomy. Of special interest are theoretical papers elucidating the state of the art of data-based transfer entropy estimation techniques.
Dr. Deniz Gencaga
Manuscript Submission Information
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- transfer entropy
- causal relationships
- entropy estimation
- statistical dependency
- nonlinear interactions
- interacting subsystems
- Granger causality
- mutual information
- machine learning
- data mining