Exact estimation for Markov chain equilibrium expectations

Peter W. Glynn, Chang-Han Rhee

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

We introduce a new class of Monte Carlo methods, which we call exact estimation algorithms. Such algorithms provide unbiased estimators for equilibrium expectations associated with real-valued functionals defined on a Markov chain. We provide easily implemented algorithms for the class of positive Harris recurrent Markov chains, and for chains that are contracting on average. We further argue that exact estimation in the Markov chain setting provides a significant theoretical relaxation relative to exact simulation methods.

Original languageEnglish (US)
Pages (from-to)377-389
Number of pages13
JournalJournal of Applied Probability
Volume51A
DOIs
StatePublished - Dec 1 2014

Keywords

  • Exact estimation
  • Exact sampling
  • Exact simulation
  • Markov chain equilibrium expectation
  • Markov chain stationary expectation
  • Perfect sampling
  • Perfect simulation
  • Unbiased estimation

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Statistics, Probability and Uncertainty

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