Multivariate statistical simulation
Multivariate statistical simulation
Generating random deviates from multivariate Pearson distributions
Computational Statistics & Data Analysis
The impact of autocorrelation on queuing systems
Management Science
The TES methodology: modeling empirical stationary time series
WSC '92 Proceedings of the 24th conference on Winter simulation
Composition for multivariate random variables
WSC '94 Proceedings of the 26th conference on Winter simulation
Recent developments in input modeling with Bézier distributions
WSC '96 Proceedings of the 28th conference on Winter simulation
Multivariate input modeling with Johnson distributions
WSC '96 Proceedings of the 28th conference on Winter simulation
Automatic modeling of file system workloads using two-level arrival processes
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Numerical Methods for Fitting and Simulating Autoregressive-To-Anything Processes
INFORMS Journal on Computing
Initialization for NORTA: Generation of Random Vectors with Specified Marginals and Correlations
INFORMS Journal on Computing
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Autoregressive to anything: Time-series input processes for simulation
Operations Research Letters
Should we model dependence and nonstationarity, and if so how?
WSC '05 Proceedings of the 37th conference on Winter simulation
Winter Simulation Conference
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An important step in designing stochastic simulation is modeling the uncertainty in the input environment of the system being studied. Obtaining a reasonable representation of this uncertainty can be challenging in the presence of dependencies in the input process. This tutorial attempts to provide a coherent narrative of the central principles that underlie methods that aim to model and sample a wide variety of dependent input processes.