Adaptive Cluster Expansion for Inferring Boltzmann Machines with Noisy Data

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.

[1]  M. Opper,et al.  Advanced mean field methods: theory and practice , 2001 .

[2]  R. Rosenfeld Nature , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[3]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[4]  G. Miller,et al.  Cognitive science. , 1981, Science.

[5]  Ericka Stricklin-Parker,et al.  Ann , 2005 .

[6]  J. M. Oshorn Proc. Nat. Acad. Sei , 1978 .

[7]  宁北芳,et al.  疟原虫var基因转换速率变化导致抗原变异[英]/Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A , 2005 .