Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests

Details Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests Adaptive cluster expansion for the inverse Ising problem: convergence, algorithm and tests

2011-10-25

arxiv.org/abs/1110.5416v1

Physics

Condensed Matter

Disordered Systems and Neural Networks

Paper submitted to Journal of Statistical Physics

Scientific paper

We present a procedure to solve the inverse Ising problem, that is to find the interactions between a set of binary variables from the measure of their equilibrium correlations. The method consists in constructing and selecting specific clusters of variables, based on their contributions to the cross-entropy of the Ising model. Small contributions are discarded to avoid overfitting and to make the computation tractable. The properties of the cluster expansion and its performances on synthetic data are studied. To make the implementation easier we give the pseudo-code of the algorithm.

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