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Metamodels and the Bootstrap for Input Model Uncertainty Analysis

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

Design and Analysis of Experiments

The distribution of simulation output statistics includes variation form the finiteness of samples used to construct input probability models. Metamodels and bootstrapping provide a way to characterize this error. The metamodel-fiting experiment benefits from a sequential design strategy. We describe the elements of such a strategy, and show how they impact performance.

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

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