Showing papers on "Uncertainty quantification published in 1997"
TL;DR: In this article, it is shown that the separation between models of randomness and uncertainty may only be conceptual or numerical but not mathematical, and two errors in previous documentation of TRCs are corrected.
3 citations
1 Jan 1997
TL;DR: In this paper, the evaluation and combination of subjective probability estimates for categorical events is discussed, and the appropriate criterion for evaluating individual and combined estimates depends on the type of uncertainty the decision maker seeks to represent, which in turn depends on his or her model of the event space.
Abstract: This paper concerns the evaluation and combination of subjective probability estimates for categorical events. We argue that the appropriate criterion for evaluating individual and combined estimates depends on the type of uncertainty the decision maker seeks to represent, which in turn depends on his or her model of the event space. Decision makers require accurate estimates in the presence of aleatory uncertainty about exchangeable events, diagnostic estimates given epistemic uncertainty about unique events, and some combination of the two when the events are not necessarily unique, but the best equivalence class definition for exchangeable events is not apparent. Following a brief reveiw of the mathematical and empirical literature on combining judgments, we present an approach to the topic that derives from (1) a weak cognitive model of the individual that assumes subjective estimates are a function of underlying judgment perturbed by random error and (2) a classification of judgment contexts in terms of the underlying information structure. In support of our developments, we present new analyses of two sets of subjective probability estimates, one of exchangeable and the other of unique events. As predicted, mean estimates were more accurate than the individual values in the first case and more diagnostic in the second. #1997 by John Wiley & Sons, Ltd.
TL;DR: Uncertainty and sensitivity analyses for systems that involve both stochastic (i.e., aleatory) and subjective uncertainty are discussed in this paper, where the dependent variable is usually a complementary cumulative distribution function.
Abstract: Uncertainty and sensitivity analyses for systems that involve both stochastic (i.e., aleatory) and subjective (i.e., epistemic) uncertainty are discussed. In such analyses, the dependent variable is usually a complementary cumulative distribution function (CCDF) that arises from stochastic uncertainty; uncertainty analysis involves the determination of a distribution of CCDFs that results from subjective uncertainty, and sensitivity analysis involves the determination of the effects of subjective uncertainty in individual variables on this distribution of CCDFs. Uncertainty analysis is presented as an integration problem involving probability spaces for stochastic and subjective uncertainty. Approximation procedures for the underlying integrals are described that provide an assessment of the effects of stochastic uncertainty, an assessment of the effects of subjective uncertainty, and a basis for performing sensitivity studies. Extensive use is made of Latin hypercube sampling, importance sampling and reg...