Multivariate statistical simulation
Multivariate statistical simulation
A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Parameter estimation of the Weibull probability distribution
Mathematics and Computers in Simulation
Input modeling when simple models fail
WSC '95 Proceedings of the 27th conference on Winter simulation
Using univariate Be´zier distributions to model simulation input processes
WSC '93 Proceedings of the 25th conference on Winter simulation
Modeling dependencies in stochastic simulation inputs
Proceedings of the 29th conference on Winter simulation
Input modeling tools for complex problems
Proceedings of the 30th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Introduction to Probability Models, Ninth Edition
Introduction to Probability Models, Ninth Edition
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
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Discrete-event simulation models typically have stochastic elements that mimic the probabilistic nature of the system under consideration. Successful input modeling requires a close match between the input model and the true underlying probabilistic mechanism associated with the system. The general question considered here is how to model an element (e.g., arrival process, service times) in a discrete-event simulation given a data set collected on the element of interest. For brevity, it is assumed that data is available on the aspect of the simulation of interest. It is also assumed that raw data is available, as opposed to censored data, grouped data, or summary statistics. Most simulation texts (e.g., Law and Kelton 2000) have a broader treatment of input modeling than presented here. Nelson et al. (1995) and Nelson and Yamnitsky (1998) survey advanced techniques.