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Metamodels and the Bootstrap for Input Model Uncertainty Analysis

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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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