Transfer entropy
Transfer entropy is a non-parametric statistic measuring the amount of directed transfer of information between two random processes. Transfer entropy from a process X to another process Y is the amount of uncertainty reduced in future values of Y by knowing the past values of X given past values of Y. More specifically, if and for denote two random processes and the amount of information is measured using Shannon's entropy, the transfer entropy can be written as:
where H is Shannon entropy of X. The above definition of transfer entropy has been extended by other types of entropy measures such as Rényi entropy.
Transfer entropy is conditional mutual information, with the history of the influenced variable in the condition:
Transfer entropy reduces to Granger causality for vector auto-regressive processes. Hence, it is advantageous when the model assumption of Granger causality doesn't hold, for example, analysis of non-linear signals. However, it usually requires more samples for accurate estimation.
The probabilities in the entropy formula can be estimated using different approaches or, in order to reduce complexity, using a non-uniform embedding.
While it was originally defined for bivariate analysis, transfer entropy has been extended to multivariate forms, either conditioning on other potential source variables or considering transfer from a collection of sources, although these forms require more samples again.
Transfer entropy has been used for estimation of functional connectivity of neurons and social influence in social networks.
Transfer entropy is a finite version of the Directed Information which was defined in 1990 by James Massey as
, where denotes the vector and denotes. The directed information places an important role in characterizing the fundamental limits of communication channels with or without feedback
and gambling with causal side information,
