INPUT UNCERTAINTY AND POTENTIAL-TO-VALIDATE: SAMPLING PLANS FOR MONTE CARLO ASSESSMENT

摘要

In complex settings, validation of mechanistic models is often difficult because appropriate input values are not precisely known. Input uncertainty limits the degree to which models can be realistically validated. The most optimistic pre-assessment of model validity, or "potential-to-validate," is closely associated with ideas and indices used in probabilistic sensitivity and uncertainty analysis. This talk will review a relevant nonparametric sampling-based approach to sensitivity/uncertainty analysis of computer models, and discuss recent work in the input sampling plans that support this kind of analysis.