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Bayesian finite mixtures of Ising models

Author

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  • Zhen Miao

    (Microsoft Corporation)

  • Yen-Chi Chen

    (University of Washington)

  • Adrian Dobra

    (University of Washington)

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 .

Suggested Citation

  • Zhen Miao & Yen-Chi Chen & Adrian Dobra, 2025. "Bayesian finite mixtures of Ising models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 88(6), pages 777-809, August.
    • Handle: RePEc:spr:metrik:v:88:y:2025:i:6:d:10.1007_s00184-024-00970-4
    DOI: 10.1007/s00184-024-00970-4
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