Response surface methodology and its application in simulation
WSC '93 Proceedings of the 25th conference on Winter simulation
Proceedings of the 30th conference on Winter simulation
Designing simulation experiments
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Simulation optimization methodologies
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
New advances for wedding optimization and simulation
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Regression metamodeling in simulation using Bayesian methods
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Proceedings of the 32nd conference on Winter simulation
Bayesian methods: bayesian methods for simulation
Proceedings of the 32nd conference on Winter simulation
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
A framework for Response Surface Methodology for simulation optimization
Proceedings of the 32nd conference on Winter simulation
Integrating optimization and simulation: research and practice
Proceedings of the 32nd conference on Winter simulation
Simulation data mining: a new form of computer simulation output
WSC '05 Proceedings of the 37th conference on Winter simulation
Balancing bias and variance in the optimization of simulation models
WSC '05 Proceedings of the 37th conference on Winter simulation
Simulation metamodels for modeling output distribution parameters
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Game-theoretic validation and analysis of air combat simulation models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans - Special issue on model-based diagnostics
Influence diagrams in analysis of discrete event simulation data
Winter Simulation Conference
Simulation metamodeling in continuous time using dynamic Bayesian networks
Proceedings of the Winter Simulation Conference
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We consider how simulation metamodels can be used to optimize the performance of a system that depends on a number of factors. We focus on the situation where the number of simulation runs that can be made is limited, and where a large number of factors must be included in the metamodel. Bayesian methods are particularly useful in this situation and can handle problems for which classical stochastic optimization can fail. We describe the basic Bayesian methodology, and then an extension to this that fits a quadratic response surface which, for function minimization, is guaranteed to be positive definite. An example is presented to illustrate the methods proposed in this paper.