Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data

Details Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data

2011-02-16

arxiv.org/abs/1102.3260v1

Physics

Data Analysis, Statistics and Probability

Accepted for publication in Physical Review Letters (2011)

Scientific paper

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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