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
Published: Oct 21, 2004
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