Management Science
Operations Research
The spatial correlation function approach to response surface estimation
WSC '92 Proceedings of the 24th conference on Winter simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Design and Modeling for Computer Experiments (Computer Science & Data Analysis)
Kriging metamodeling in constrained simulation optimization: an explorative study
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Design of experiments: overview
Proceedings of the 40th Conference on Winter Simulation
An Efficient and Adaptive Mechanism for Parallel Simulation Replication
PADS '09 Proceedings of the 2009 ACM/IEEE/SCS 23rd Workshop on Principles of Advanced and Distributed Simulation
Stochastic Kriging for Simulation Metamodeling
Operations Research
Simulation optimization using metamodels
Winter Simulation Conference
Winter Simulation Conference
Automating the runtime performance evaluation of simulation algorithms
Winter Simulation Conference
Cognitive policy learner: biasing winning or losing strategies
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
The effects of common random numbers on stochastic kriging metamodels
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimization via simulation with Bayesian statistics and dynamic programming
Proceedings of the Winter Simulation Conference
Common random numbers and stochastic kriging
Proceedings of the Winter Simulation Conference
Game theoretic simulation metamodeling using stochastic kriging
Proceedings of the Winter Simulation Conference
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We extend the basic theory of kriging, as applied to the design and analysis of deterministic computer experiments, to the stochastic simulation setting. Our goal is to provide flexible, interpolation-based metamodels of simulation output performance measures as functions of the controllable design or decision variables. To accomplish this we characterize both the intrinsic uncertainty inherent in a stochastic simulation and the extrinsic uncertainty about the unknown response surface. We use tractable examples to demonstrate why it is critical to characterize both types of uncertainty, derive general results for experiment design and analysis, and present a numerical example that illustrates the stochastic kriging method.