Estimating and Applying Uncertainty in Assessment models
This chapter discusses probabilistic methods for conducting uncertainty analysis, methods that can be use to evaluate both local and global sensitivity of models to parameters, and issues related to the validation of models that express uncertainty in their results. Analytical and Monte Carlo methods for propagating uncertainty through models are described, along with potential limitations of these methods and the problems that can be encountered. The chapter introduces methods for assigning distributions to model parameters. Statistical methods that can be used to help interpret and express the results of probabilistic uncertainty analyses, such as confidence and tolerance intervals, are introduced and their pertinent assumptions are described. Various statistical analyses that can be used for sensitivity analysis and their associated sampling designs are reviewed.
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