On confidence intervals from simulation of finite Markov chains

Apostolos N. Burnetas; Michael N. Katehakis

doi:10.1007/BF01217693

On confidence intervals from simulation of finite Markov chains

Apostolos N. Burnetas, Michael N. Katehakis

Rutgers Business School, Management & Information Systems

Research output: Contribution to journal › Article › peer-review

Abstract

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 language	English (US)
Pages (from-to)	241-250
Number of pages	10
Journal	Mathematical Methods of Operations Research
Volume	46
Issue number	2
DOIs	https://doi.org/10.1007/BF01217693
State	Published - 1997

All Science Journal Classification (ASJC) codes

Software
Mathematics(all)
Management Science and Operations Research

Keywords

Discrete Markov Chains
Simulation

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10.1007/BF01217693

Cite this

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title = "On confidence intervals from simulation of finite Markov chains",

abstract = "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.",

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AU - Burnetas, Apostolos N.

AU - Katehakis, Michael N.

PY - 1997

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N2 - 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.

AB - 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.

KW - Discrete Markov Chains

KW - Simulation

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U2 - 10.1007/BF01217693

DO - 10.1007/BF01217693

M3 - Article

AN - SCOPUS:5244337928

SN - 1432-2994

VL - 46

SP - 241

EP - 250

JO - Mathematical Methods of Operations Research

JF - Mathematical Methods of Operations Research

IS - 2

ER -

On confidence intervals from simulation of finite Markov chains

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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