Likelihood Ratio Sensitivity Analysis for Markovian Models of Highly Dependable Systems

Marvin K. Nakayama, Ambuj Goyal and Peter W. Glynn
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Marvin K. Nakayama: IBM T. J. Watson Research Center, Yorktown Heights, New York
Ambuj Goyal: Rutgers University, Newark, New Jersey
Peter W. Glynn: Stanford University, Stanford, California

Operations Research, 1994, vol. 42, issue 1, 137-157

Abstract: This paper discusses the application of the likelihood ratio gradient estimator to simulations of large Markovian models of highly dependable systems. Extensive empirical work, as well as some mathematical analysis of small dependability models, suggests that (in this model setting) the gradient estimators are not significantly more noisy than the estimates of the performance measures themselves. The paper also discusses implementation issues associated with likelihood ratio gradient estimation, as well as some theoretical complements associated with application of the technique to continuous-time Markov chains.

Keywords: probability; stochastic model applications: highly dependable systems; simulation: statistical analysis of derivative estimates; simulation; efficiency: importance sampling (search for similar items in EconPapers)
Date: 1994
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Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:42:y:1994:i:1:p:137-157

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