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Optimal design via curve fitting of Monte Carlo experiments.
- Article from:
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Journal of the American Statistical Association
- Article date:
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December 1, 1995
- Author:
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;
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Copyright informationCOPYRIGHT 1995 American Statistical Association. This material is published under license from the publisher through the Gale Group, Farmington Hills, Michigan. All inquiries regarding rights should be directed to the Gale Group. (Hide copyright information)
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Numerical methods for stochastic optimization focusing on Bayesian design are proposed. The approach is based on transforming a complex numerical problem into a simpler statistical problem. The key to the solution strategy is a four-step sequence of simulating, modeling, smoothing and optimizing. The proposed algorithm relies on a curve-fitting procedure.
This article explores numerical methods for stochastic optimization, with special attention to Bayesian design problems. A common and challenging situation occurs when the objective function (in Bayesian applications, the expected utility) is very expensive to evaluate, perhaps because it requires integration over a space of very ...
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