Block updating in constrained Markov chain Monte Carlo sampling

Merrilee A. Hurn*, Håvard Rue, Nuala A. Sheehan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Markov chain Monte Carlo methods are widely used to study highly structured stochastic systems. However, when the system is subject to constraints, it is difficult to find irreducible proposal distributions. We suggest a "block-wise" approach for constrained sampling and optimisation.

Original languageEnglish (US)
Pages (from-to)353-361
Number of pages9
JournalStatistics and Probability Letters
Issue number2
StatePublished - Feb 15 1999


  • Constrained distributions
  • Importance sampling
  • Irreducibility
  • Markov chain monte carlo
  • Multiple-site updating
  • Stochastic simulation and optimisation

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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