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Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data

Phys. Rev. Lett. 106, 090601 – Published 2 March 2011
S. Cocco and R. Monasson

Abstract

We introduce a procedure to infer the interactions among a set of binary variables, based on their sampled frequencies and pairwise correlations. The algorithm builds the clusters of variables contributing most to the entropy of the inferred Ising model and rejects the small contributions due to the sampling noise. Our procedure successfully recovers benchmark Ising models even at criticality and in the low temperature phase, and is applied to neurobiological data.

DOI: http://dx.doi.org/10.1103/PhysRevLett.106.090601

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  • Received 24 August 2010
  • Published 2 March 2011

© 2011 American Physical Society

Authors & Affiliations

S. Cocco1,2 and R. Monasson1,3

  • 1The Simons Center for Systems Biology, Institute for Advanced Study, Einstein Drive, Princeton, New Jersey 08540, USA
  • 2CNRS-Laboratoire de Physique Statistique de l’ENS, 24 rue Lhomond, 75005 Paris, France
  • 3CNRS-Laboratoire de Physique Théorique de l’ENS, 24 rue Lhomond, 75005 Paris, France

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