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arXiv · 1107.3536

Inverse Ising inference using all the data

Erik Aurell·Magnus Ekeberg
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Abstract

We show that a method based on logistic regression, using all the data, solves the inverse Ising problem far better than mean-field calculations relying only on sample pairwise correlation functions, while still computationally feasible for hundreds of nodes. The largest improvement in reconstruction occurs for strong interactions. Using two examples, a diluted Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that interaction topologies can be recovered from few samples with good accuracy and that the use of $l_1$-regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.

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Erik Aurell, Magnus Ekeberg. 2012-11-12. Inverse Ising inference using all the data. https://doi.org/10.1103/physrevlett.108.090201

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