The Self-Multiset Sampler

Weihong Huang, Juan Shen, Yuguo Chen

Research output: Contribution to journalArticlepeer-review


The multiset sampler has been shown to be an effective algorithm to sample from complex multimodal distributions, but the multiset sampler requires that the parameters in the target distribution can be divided into two parts: the parameters of interest and the nuisance parameters. We propose a new self-multiset sampler (SMSS), which extends the multiset sampler to distributions without nuisance parameters. We also generalize our method to distributions with unbounded or infinite support. Numerical results show that the SMSS and its generalization have a substantial advantage in sampling multimodal distributions compared to the ordinary Markov chain Monte Carlo algorithm and some popular variants. Supplemental materials for the article are available online.

Original languageEnglish (US)
Pages (from-to)34-47
Number of pages14
JournalJournal of Computational and Graphical Statistics
Issue number1
StatePublished - Jan 2 2018


  • Data augmentation
  • Metropolis–Hastings algorithm
  • Multimodal
  • Multiset

ASJC Scopus subject areas

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty


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