Abstract
Maximum pseudolikelihood method has been among the most important methods for learning parameters of statistical physics models, such as Ising models. In this paper, we study how pseudolikelihood can be derived for learning parameters of a mixture of Ising models. The performance of the proposed approach is demonstrated for Ising and Potts models on both synthetic and real data.
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