Simulation-based estimation of proportions
Management Science
Selecting the best system: a decision-theoretic approach
Proceedings of the 29th conference on Winter simulation
Sequential allocations that reduce risk for multiple comparisons
Proceedings of the 30th conference on Winter simulation
Simulation-based estimation of quantiles
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Input modeling: input model uncertainty: why do we care and what should we do about it?
Proceedings of the 35th conference on Winter simulation: driving innovation
Experimental performance evaluation of histogram approximation for simulation output analysis
WSC '04 Proceedings of the 36th conference on Winter simulation
Determination of the "best" system that meets a limit standard
WSC '05 Proceedings of the 37th conference on Winter simulation
Bayesian ideas and discrete event simulation: why, what and how
Proceedings of the 38th conference on Winter simulation
Comparison of limit standards using a sequential probability ratio test
Proceedings of the 38th conference on Winter simulation
Comparison of Bayesian priors for highly reliable limit models
Proceedings of the 40th Conference on Winter Simulation
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Limit standards are probabilistic requirements or benchmarks regarding the proportion of replications conforming or not conforming to a desired threshold. Sample proportions resulting from the analysis of replications are known to be beta distributed. As a result, standard constructs for defining a confidence interval on such a proportion, based on critical points from the normal or Student's t distribution, are increasingly inaccurate as the mean sample proportion approaches the limits of 0 or 1. We consider the Bayesian relationship between the beta and binomial distributions as the foundation for a sequential methodology in the analysis of limit standards. The benefits of using the beta distribution methodology are variance reduction, and smaller sample size (when compared to other analysis methodologies).