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Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data

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.
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https://hal.archives-ouvertes.fr/hal-00566281
Contributor : Simona Cocco <>
Submitted on : Tuesday, February 15, 2011 - 5:57:34 PM
Last modification on : Thursday, December 10, 2020 - 12:36:18 PM
Long-term archiving on: : Monday, May 16, 2011 - 3:22:26 AM

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  • HAL Id : hal-00566281, version 1
  • ARXIV : 1102.3260

Citation

Simona Cocco, Rémi Monasson. Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data. 2011. ⟨hal-00566281⟩

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