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
DOI: http://dx.doi.org/10.1103/PhysRevLett.108.090201
- Received 29 September 2011
- Revised 12 December 2011
- Published 1 March 2012
© 2012 American Physical Society