Modeling input processes with Johnson distributions
WSC '89 Proceedings of the 21st conference on Winter simulation
Organ transplantation policy evaluation
WSC '95 Proceedings of the 27th conference on Winter simulation
Recent developments in input modeling with Bézier distributions
WSC '96 Proceedings of the 28th conference on Winter simulation
Proceedings of the 32nd conference on Winter simulation
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Simulation Modeling and Analysis (McGraw-Hill Series in Industrial Engineering and Management)
Introduction to modeling and generating probabilistic input processes for simulation
Proceedings of the 38th conference on Winter simulation
An Automated Multiresolution Procedure for Modeling Complex Arrival Processes
INFORMS Journal on Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Numerical Recipes 3rd Edition: The Art of Scientific Computing
Smooth flexible models of nonhomogeneous poisson processes using one or more process realizations
Proceedings of the 40th Conference on Winter Simulation
Statistical analysis of non-stationary series of events in a data base system
IBM Journal of Research and Development
Introduction to financial risk assessment using Monte Carlo simulation
Winter Simulation Conference
A tutorial on how to select simulation input probability distributions
Proceedings of the Winter Simulation Conference
How to select simulation input probability distributions
Proceedings of the Winter Simulation Conference
Introduction to simulation input modeling
Proceedings of the Winter Simulation Conference
Modeling clustered non-stationary Poisson processes for stochastic simulation inputs
Computers and Industrial Engineering
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Techniques are presented for modeling, fitting, and generating many of the univariate probabilistic input processes that drive discrete-event simulation experiments. Emphasis is given to the generalized beta distribution family, the Johnson translation system of distributions, and the Bézier distribution family because of the flexibility of these families to model a wide range of distributional shapes that arise in practical applications. Also discussed are nonparametric and semiparametric techniques for modeling and simulating time-dependent arrival streams using nonhomogeneous Poisson processes. Public-domain software implementations and current applications are presented for each input-modeling technique. The applications range from pharmaceutical manufacturing and medical decision analysis to smart-materials research and healthcare systems analysis. Many of the references include live hyperlinks providing online access to the referenced material.