University of Cambridge > > Isaac Newton Institute Seminar Series > Enhancing Stochastic Kriging Metamodels with Stochastic Gradient Estimators

Enhancing Stochastic Kriging Metamodels with Stochastic Gradient Estimators

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If you have a question about this talk, please contact Mustapha Amrani.

Design and Analysis of Experiments

Stochastic kriging is the natural extension of kriging metamodels for the design and analysis of computer experiments to the design and analysis of stochastic simulation experiments where response variance may differ substantially across the design space. In addition to estimating the mean response, it is sometimes possible to obtain an unbiased or consistent estimator of the response-surface gradient from the same simulation runs. However, like the response itself, the gradient estimator is noisy. In this talk we present methodology for incorporating gradient estimators into response surface prediction via stochastic kriging, evaluate its effectiveness in improving prediction, and specifically consider two gradient estimators: the score function/likelihood ratio method and infinitesimal perturbation analysis.

This talk is part of the Isaac Newton Institute Seminar Series series.

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