Granger causality and the inverse Ising problem
Neurons and Cognition
2015-05-18 v2 Disordered Systems and Neural Networks
Data Analysis, Statistics and Probability
Abstract
We study Ising models for describing data and show that autoregressive methods may be used to learn their connections, also in the case of asymmetric connections and for multi-spin interactions. 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.
Cite
@article{arxiv.1003.4217,
title = {Granger causality and the inverse Ising problem},
author = {Mario Pellicoro and Sebastiano Stramaglia},
journal= {arXiv preprint arXiv:1003.4217},
year = {2015}
}
Comments
6 pages and 8 figures. Revised version in press on Physica A