Multivariate statistical simulation
Multivariate statistical simulation
A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
Modeling input processes with Johnson distributions
WSC '89 Proceedings of the 21st conference on Winter simulation
Multivariate input modeling with Johnson distributions
WSC '96 Proceedings of the 28th conference on Winter simulation
Estimating and simulating Poisson processes with trends or asymmetric cyclic effects
Proceedings of the 29th conference on Winter simulation
Input modeling tools for complex problems
Proceedings of the 30th conference on Winter simulation
An approximate method for generating asymmetric random variables
Communications of the ACM
Input modeling: answers to the top ten input modeling questions
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Building credible input models
WSC '04 Proceedings of the 36th 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)
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
Introduction to modeling and generating probabilistic input processes for simulation
Winter Simulation Conference
A tutorial on how to select simulation input probability distributions
Proceedings of the Winter Simulation Conference
A tutorial on simulation modeling in six dimensions
Proceedings of the Winter Simulation Conference
How to select simulation input probability distributions
Proceedings of the Winter Simulation Conference
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In this tutorial we first review introductory techniques for simulation input modeling. We then identify situations in which the standard input models fail to adequately represent the available input data. In particular, we consider the cases where the input process may (i) have marginal characteristics that are not captured by standard distributions; (ii) exhibit dependence; and (iii) change over time. For case (i), we review flexible distribution systems, while we review two widely used multivariate input models for case (ii). Finally, we review nonhomogeneous Poisson processes for the last case. We focus our discussion around continuous random variables; however, when appropriate references are provided for discrete random variables. Detailed examples will be illustrated in the tutorial presentation.