The hamming ball sampler

Michalis K Titsias, Christopher Yau

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
215 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)1598-1611
Number of pages14
JournalJournal of American Statistical Association
Volume112
Issue number520
DOIs
Publication statusPublished - 18 Jul 2017

Fingerprint

Dive into the research topics of 'The hamming ball sampler'. Together they form a unique fingerprint.

Cite this