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Decision-Theoretic Sensitivity Analysis for Complex Computer Models

Summary: [This abstract is based on the author’s abstract.] When using a computer model to support a decision, it is important to investigate any uncertainty in the model and how it may affect the decision. Much of the literature focuses on output uncertainty as measured by variance, rather than on the decision problem itself. As a result, traditional variance-based measures of input parameter importance may not correctly describe the importance of each input. A decision-theoretic framework is considered for conducting sensitivity analysis that addresses this problem. Computational tools using Gaussian processes are provided. An illustration compares the Gaussian process approach with conventional Monte Carlo sampling.

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  • Topics: Information Management, Sampling
  • Keywords: Bayesian methods, Computers, Gaussian curve, Decision making, Uncertainty, Sensitivity analysis
  • Author: Oakley, Jeremy E.
  • Journal: Technometrics