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• W2097508093 abstract "The large amounts of data from molecular biology and neuroscience have lead to a renewed interest in the inverse Ising problem: how to reconstruct parameters of the Ising model (couplings between spins and external fields) from a number of spin configurations sampled from the Boltzmann measure. To invert the relationship between model parameters and observables (magnetisations and correlations) mean-field approximations are often used, allowing to determine model parameters from data. However, all known mean-field methods fail at low temperatures with the emergence of multiple thermodynamic states. Here we show how clustering spin configurations can approximate these thermodynamic states, and how mean-field methods applied to thermodynamic states allow an efficient reconstruction of Ising models also at low temperatures." @default.
• W2097508093 created "2016-06-24" @default.
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• W2097508093 date "2012-08-01" @default.
• W2097508093 modified "2023-10-16" @default.
• W2097508093 title "Mean-Field Theory for the Inverse Ising Problem at Low Temperatures" @default.
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• W2097508093 doi "https://doi.org/10.1103/physrevlett.109.050602" @default.
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