Bayesian finite mixtures of Ising models

Zhen Miao (), Yen-Chi Chen () and Adrian Dobra ()
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Zhen Miao: Microsoft Corporation
Yen-Chi Chen: University of Washington
Adrian Dobra: University of Washington

Metrika: International Journal for Theoretical and Applied Statistics, 2025, vol. 88, issue 6, No 3, 777-809

Abstract: Abstract We introduce finite mixtures of Ising models as a novel approach to study multivariate patterns of associations of binary variables. Our proposed models combine the strengths of Ising models and multivariate Bernoulli mixture models. We examine conditions required for the local identifiability of Ising mixture models, and develop a Bayesian framework for fitting them. Through simulation experiments and real data examples, we show that Ising mixture models lead to meaningful results for sparse binary contingency tables with imbalanced cell counts. The code necessary to replicate our empirical examples is available on GitHub: https://github.com/Epic19mz/BayesianIsingMixtures .

Keywords: Bayesian; Finite mixture model; Ising model; Multivariate binary data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00184-024-00970-4

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