On confidence intervals from simulation of finite Markov chains

Apostolos N. Burnetas, Michael N. Katehakis

Research output: Contribution to journalArticlepeer-review


Consider a finite state irreducible Markov reward chain. It is shown that there exist simulation estimates and confidence intervals for the expected first passage times and rewards as well as the expected average reward, with 100% coverage probability. The length of the confidence intervals converges to zero with probability one as the sample size increases; it also satisfies a large deviations property.

Original languageEnglish (US)
Pages (from-to)241-250
Number of pages10
JournalMathematical Methods of Operations Research
Issue number2
StatePublished - 1997

All Science Journal Classification (ASJC) codes

  • Software
  • Mathematics(all)
  • Management Science and Operations Research


  • Discrete Markov Chains
  • Simulation


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