On confidence intervals from simulation of finite Markov chains

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. Copyright Physica-Verlag 1997

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Year of publication:	1997
Authors:	Burnetas, Apostolos ; Katehakis, Michael
Published in:	Mathematical Methods of Operations Research. - Springer. - Vol. 46.1997, 2, p. 241-250
Publisher:	Springer
Subject:	Discrete Markov Chains \| Simulation

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

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