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Dependencies between variables or processes in systems are often quantified using correlation coefficients (income <-> height, etc.). However, correlation only captures linear dependencies. In addition the dynamical aspect (e.g. common history) and the direction of information transfer in the processes under consideration are neglected. A recently proposed measure called '''Transfer Entropy''' was specifically designed to overcome those limitations by the clever use of conditioned transition probabilities.
Dependencies between variables or processes in systems are often quantified using correlation coefficients (income <-> height, etc.). However, correlation only captures linear dependencies. In addition the dynamical aspect (e.g. common history) and the direction of information transfer in the processes under consideration are neglected. A recently proposed measure called '''Transfer Entropy''' was specifically designed to overcome those limitations by the clever use of conditioned transition probabilities.


In theory, Transfer Entropy, is able to quantify non-/linear directed dependencies in any dynamical system. In practice, the transition probabilities/-densities have to be estimated from the data. That's the reason, why we need your data! If you happen to have good multivariate time series data (e.g. bio-chemical/medical/logical dynamical systems, longitudinal studies in sociology, etc.), which you expect to be holding interesting dependencies of any kind, let us know.
In theory, Transfer Entropy, is able to quantify non-/linear directed dependencies in any dynamical system. In practice, the transition probabilities/-densities have to be estimated from the data.
 
That's the reason, why '''we need your data'''! If you happen to have good multivariate time series data (e.g. bio-chemical/medical/logical dynamical systems, longitudinal studies in sociology, etc.), which you expect to be holding interesting dependencies of any kind, let us know.




==Team members (feel free to join)==  
==Team members (feel free to join)==  


* Juergen Pahle
* [[Juergen_Pahle]]
* Joseph Lizier
* [[Joseph_Lizier]]
 


==Readings==
==Readings==


* T. Schreiber (2000) Measuring information transfer. Phys. Rev. Lett '''85'''(2), 461-4
* T. Schreiber (2000) Measuring information transfer. Phys. Rev. Lett '''85'''(2), 461-4
 
Download at [[http://prola.aps.org/abstract/PRL/v85/i2/p461_1]] (requires access to PRL).


==Questions to answer==
==Questions to answer==
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* What can we learn about (possibly nonlinear) dependencies between variables in biomedical, ecological, sociological ... systems
* What can we learn about (possibly nonlinear) dependencies between variables in biomedical, ecological, sociological ... systems
* How does information transfer measured by the transfer entropy breaks down in systems, on which damage was inflicted (ischemia in heart, extinction of species in dynamical foodwebs etc.)
* How does information transfer measured by the transfer entropy breaks down in systems, on which damage was inflicted (ischemia in heart, extinction of species in dynamical foodwebs etc.)
* ...
==Ideas==
* Measure information transfer in the heart model by Vikas, Simon, Nathan & Co.
* ...
* ...

Latest revision as of 01:23, 19 June 2007

CSSS Santa Fe 2007

Background

Dependencies between variables or processes in systems are often quantified using correlation coefficients (income <-> height, etc.). However, correlation only captures linear dependencies. In addition the dynamical aspect (e.g. common history) and the direction of information transfer in the processes under consideration are neglected. A recently proposed measure called Transfer Entropy was specifically designed to overcome those limitations by the clever use of conditioned transition probabilities.

In theory, Transfer Entropy, is able to quantify non-/linear directed dependencies in any dynamical system. In practice, the transition probabilities/-densities have to be estimated from the data.

That's the reason, why we need your data! If you happen to have good multivariate time series data (e.g. bio-chemical/medical/logical dynamical systems, longitudinal studies in sociology, etc.), which you expect to be holding interesting dependencies of any kind, let us know.


Team members (feel free to join)

Readings

  • T. Schreiber (2000) Measuring information transfer. Phys. Rev. Lett 85(2), 461-4

Download at [[1]] (requires access to PRL).

Questions to answer

  • What can we learn about (possibly nonlinear) dependencies between variables in biomedical, ecological, sociological ... systems
  • How does information transfer measured by the transfer entropy breaks down in systems, on which damage was inflicted (ischemia in heart, extinction of species in dynamical foodwebs etc.)
  • ...

Ideas

  • Measure information transfer in the heart model by Vikas, Simon, Nathan & Co.
  • ...