Introduction to operations research, 4th ed.
Introduction to operations research, 4th ed.
Computational efficiency of batching methods
Proceedings of the 29th conference on Winter simulation
Improved batching for confidence interval construction in steady-state simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
The Art of Computer Programming Volumes 1-3 Boxed Set
The Art of Computer Programming Volumes 1-3 Boxed Set
Output analysis: ASAP2: an improved batch means procedure for simulation output analysis
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Simulation output analysis: a wavelet-based spectral method for steady-state simulation analysis
Proceedings of the 35th conference on Winter simulation: driving innovation
A Bayesian approach to analysis of limit standards
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Comparison of Bayesian priors for highly reliable limit models
Proceedings of the 40th Conference on Winter Simulation
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We summarize the results of an experimental performance evaluation of using an empirical histogram to approximate the steady-state distribution of the underlying stochastic process. We use a runs test to determine the required sample size for simulation output analysis and construct a histogram by computing sample quantiles at certain grid points. The algorithm dynamically increases the sample size so that histogram estimates are asymptotically unbiased. Characteristics of the steady-state distribution, such as the mean and variance, can then be estimated through the empirical histogram. The preliminary experimental results indicate that the natural estimators obtained based on the empirical distribution are fairly accurate.