WSC '94 Proceedings of the 26th conference on Winter simulation
Graphical interactive simulation input modeling with bivariate Bézier distributions
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on graphics, animation, and visualization for simulation environments
Uniform and bootstrap resampling of empirical distributions
WSC '93 Proceedings of the 25th conference on Winter simulation
Empirical input distributions: an alternative to standard input distribution in simulation modeling
WSC '91 Proceedings of the 23rd conference on Winter simulation
Proceedings of the 32nd conference on Winter simulation
VV&A; IV: validation of trace-driven simulation models: more on bootstrap tests
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
The generation of order statistics in digital computer simulation: A survey
WSC '78 Proceedings of the 10th conference on Winter simulation - Volume 1
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
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
Reliable simulation with input uncertainties using an interval-based approach
Proceedings of the 40th Conference on Winter Simulation
Winter Simulation Conference
Robust Simulation of Global Warming Policies Using the DICE Model
Management Science
Input uncertainty in outout analysis
Proceedings of the Winter Simulation Conference
Capturing parameter uncertainty in simulations with correlated inputs
Proceedings of the Winter Simulation Conference
A framework for input uncertainty analysis
Proceedings of the Winter Simulation Conference
Hi-index | 0.00 |
Stochastic simulation models are used to predict the behavior of real systems whose components have random variation. The simulation model generates artificial random quantities based on the nature of the random variation in the real system. Very often, the probability distributions occurring in the real system are unknown, and must be estimated using finite samples. This paper shows three methods for incorporating the error due to input distributions that are based on finite samples, when calculating confidence intervals for output parameters.