Granger causality and the inverse Ising problem

Mario Pellicoro and Sebastiano Stramaglia

Physica A: Statistical Mechanics and its Applications, 2010, vol. 389, issue 21, 4747-4754

Abstract: The inference of the couplings of an Ising model with given means and correlations is called the inverse Ising problem. This approach has received a lot of attention as a tool to analyze neural data. We show that autoregressive methods may be used to learn the couplings of an Ising model, also in the case of asymmetric connections and for multispin interactions. We find that, for each link, the linear Granger causality is two times the corresponding transfer entropy (i.e., the information flow on that link) in the weak coupling limit. For sparse connections and a low number of samples, the ℓ1 regularized least squares method is used to detect the interacting pairs of spins. Nonlinear Granger causality is related to multispin interactions.

Keywords: Granger causality; Transfer entropy; Ising model (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:389:y:2010:i:21:p:4747-4754

DOI: 10.1016/j.physa.2010.06.028

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