The Hamming Ball Sampler

Michalis K. Titsias and Christopher Yau

Journal of the American Statistical Association, 2017, vol. 112, issue 520, 1598-1611

Abstract: We introduce the Hamming ball sampler, a novel Markov chain Monte Carlo algorithm, for efficient inference in statistical models involving high-dimensional discrete state spaces. The sampling scheme uses an auxiliary variable construction that adaptively truncates the model space allowing iterative exploration of the full model space. The approach generalizes conventional Gibbs sampling schemes for discrete spaces and provides an intuitive means for user-controlled balance between statistical efficiency and computational tractability. We illustrate the generic utility of our sampling algorithm through application to a range of statistical models. Supplementary materials for this article are available online.

Date: 2017
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DOI: 10.1080/01621459.2016.1222288

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