Article ID: | iaor1995313 |
Country: | United States |
Volume: | 42 |
Issue: | 1 |
Start Page Number: | 137 |
End Page Number: | 157 |
Publication Date: | Jan 1994 |
Journal: | Operations Research |
Authors: | Glynn Peter W., Nakayama Marvin K., Goyal Ambuj |
Keywords: | simulation: analysis, markov processes, statistics: empirical |
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.