On random- and systematic-scan samplers

Research output: Contribution to journalArticle (Academic Journal)peer-review

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We introduce a simple time-homogeneous Markov embedding of a class of time-inhomogeneous Markov chains widely used in the context of Monte Carlo sampling algorithms, such as systematic-scan Metropolis-within-Gibbs samplers. This allows us to establish that systematic-scan samplers involving two factors are always better than their random-scan counterparts, when asymptotic variance is the criterion of interest. We also show that this embedding sheds some light on the result of Maire et al. (2014) and discuss the scenario involving more than two factors.
Original languageEnglish
Pages (from-to)719-726
Number of pages8
Issue number3
Early online date24 Aug 2016
Publication statusPublished - Sept 2016


  • Deterministic-scan sampler
  • Markov chain Monte Carlo
  • Metropolis-within-Gibbs algorithm
  • Peskun order
  • Random-scan sampler


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