Reliable Computing: Special Issue on Dependable Reasoning about Uncertainty

Reliable Computing: Special Issue on Dependable Reasoning about Uncertainty Reliable Computing: Special Issue on Dependable Reasoning about Uncertainty GUEST EDITOR: DANIEL BERLEANT Reliable Computing, the journal of interval mathematics and reliable numerical computations, will devote a special issue to papers that address dependable han- dling of uncertainty. Diverse problems in important fields such as risk, reliability, measurement interpretation, signal processing, control, and decision theory, and in applications as varied as insurance and finance, mathematical ecology, and many others can benefit from inference under conditions in which uncertainty is inher- ent in the problem, and further, in which understanding of the uncertainty itself is vague, conflicting, or otherwise incomplete. Traditional approaches, such as mak- ing assumptions as needed to allow problem solution by traditional means, are often thought necessary or used without sufficient attention to the dependability of the results. Due to such problems, there is growing interest as well as a growing body of results on innovative ways to dependably handle uncertainties that are incompletely characterized, vague, imprecise, etc. Relevant work falls under various overlapping subject categories. A non- exhaustive list of such relevant areas would include, for example, interval-valued probabilities, dependency bounds, given marginals, rough sets, stochastic num- bers, robust statistics, upper and lower previsions, imprecise probabilities, Reliable Computing Springer Journals

Reliable Computing: Special Issue on Dependable Reasoning about Uncertainty

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Kluwer Academic Publishers
Copyright © 2002 by Kluwer Academic Publishers
Mathematics; Numeric Computing; Approximations and Expansions; Computational Mathematics and Numerical Analysis; Mathematical Modeling and Industrial Mathematics
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