Uncertainty analysis with uncertain input distributions: applied to a road traffic emission model
(English)Manuscript (Other academic)
The output of a model or an experiment is often fairly uncertain because the underlying conditions have to some extent been guessed at or estimated. Such uncertainty is usually described by examining the distribution of the output given an input of known distribution. In this paper, we discuss why this approachmay not be suitable for some problems, and consider an alternate approach in cases in which we have only vague information concerning the distributions of the inputs, regardless as to whether these inputs are continm:ms or categorical. Our approach is well suited for a mean output that represents the weighted sum of the outputs of categories, if there is a large dataset with an uncertain input distribution. In this study, the method is applied to a road traffic emission scenario.
IdentifiersURN: urn:nbn:se:liu:diva-14299OAI: oai:DiVA.org:liu-14299DiVA: diva2:23144