The Hamming Ball Sampler

• 成果类型: Article
• 署名作者: Titsias, Michalis K.; Yau, Christopher
• 署名单位: Athens University of Economics & Business; University of Oxford; Wellcome Centre for Human Genetics; University of Oxford
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1222288
• 发表日期: 2017
• 页码: 1598-1611
• 关键词: monte-carlo; stochastic search

摘要

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.