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• W2161642962 abstract "Computational models whose internal details are not accessible to the analyst are called black boxes. They arise because of security restrictions or because of the loss of the source code for legacy software programs. Computational models whose internal details are extremely complex are also sometimes treated as black boxes. It is often important to assess the uncertainty that should be ascribed to the output from a black box owing to uncertainty about its input quantities, their statistical distributions, or interdependencies. Sensitivity or ‘what-if’ studies are commonly used for this purpose. In such studies, the space of possible inputs is sampled as a vector of real values which is then provided to the black box to compute the output(s) that corresponds to those inputs. Such studies are often cumbersome to implement and understand, and they generally require many samples, depending on the complexity of the model and the dimensionality of the inputs. This report reviews methods that can be used to propagate about inputs through black boxes, especially ‘hard’ black boxes whose computational complexity restricts the total number of samples that can be evaluated. The focus is on methods that estimate the uncertainty of the outputs from the outside inward. That is, we are interested in methods that produce conservative characterizations of uncertainty that become tighter and tighter as the total computational effort increases." @default.
• W2161642962 created "2016-06-24" @default.
• W2161642962 creator A5029294781 @default.
• W2161642962 creator A5040035817 @default.
• W2161642962 date "2008-01-01" @default.
• W2161642962 modified "2023-09-26" @default.
• W2161642962 title "Projecting uncertainty through black boxes" @default.
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