A theory for the multiset sampler

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The multiset sampler (MSS) can be viewed as a new data augmentation scheme and it has been applied successfully to a wide range of statistical inference problems. The key idea of the MSS is to augment the system with a multiset of the missing components, and construct an appropriate joint distribution of the parameters of interest and the missing components to facilitate the inference based on Markov chain Monte Carlo. The standard data augmentation strategy corresponds to the MSS with multiset size one. This paper provides a theoretical comparison of the MSS with different multiset sizes. We show that the MSS converges to the target distribution faster as the multiset size increases. This explains the improvement in convergence rate for the MSS with large multiset sizes over the standard data augmentation scheme.

Original languageEnglish (US)
Pages (from-to)473-477
Number of pages5
JournalStatistics and Probability Letters
Issue number3
StatePublished - Mar 2012


  • Data augmentation
  • Evolutionary forest algorithm
  • Forward operator
  • Gibbs sampler
  • Markov chain Monte Carlo

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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