Matrix analysis
Operations Research
Some guidelines and guarantees for common random numbers
Management Science
Integrated variance reduction strategies
WSC '93 Proceedings of the 25th conference on Winter simulation
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Correlation induction without the inverse transformation
WSC '86 Proceedings of the 18th conference on Winter simulation
Convergence theory for nonconvex stochastic programming with an application to mixed logit
Mathematical Programming: Series A and B
The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
INFORMS Journal on Computing
Sharpening comparisons via gaussian copulas and semidefinite programming
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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Suppose one wishes to compare two closely related systems via stochastic simulation. Common random numbers (CRN) involves using the same streams of uniform random variates as inputs for both systems to sharpen the comparison. One can view CRN as a particular choice of copula that gives the joint distribution of the inputs of both systems. We discuss the possibility of using more general copulae, including simple examples that show how this can outperform CRN.