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Many Bayesian Confirmation Measures have been proposed so far. They are used to assess the degree to which an evidence (or premise) E supports or contradicts an hypothesis (or conclusion) H, making use of prior probability P(H), posterior probability P(H|E) and of probability of evidence P(E)....
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This paper attempts to provide a decision-theoretic foundation for the measurement of economic tail risk, which is not … only closely related to utility theory but also relevant to statistical model uncertainty. The main result is that the only …
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Most poverty studies build on measures that take account of recurring incomes from sources such as labour or social transfers. However, other financial resources such as savings and assets also affect living standards, often in very significant ways. Previous studies that have sought to...
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In risk analysis, sensitivity measures quantify the extent to which the probability distribution of a model output is affected by changes (stresses) in individual random input factors. For input factors that are statistically dependent, we argue that a stress on one input should also precipitate...
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In a quantitative model with uncertain inputs, the uncertainty of the output can be summarized by a risk measure. We propose a sensitivity analysis method based on derivatives of the output risk measure, in the direction of model inputs. This produces a global sensitivity measure, explicitly...
Persistent link: https://www.econbiz.de/10013034689