Titlebook: Quantified Representation of Uncertainty and Imprecision; Philippe Smets Book 1998 Springer Science+Business Media Dordrecht 1998 fuzzy.fu

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The Transferable Belief Model for Quantified Belief Representation,antified credibility, subjective support, strength of opinion... These beliefs are not categorical as in modal logic, but admits degrees as in probability theory. Our approach is normative. The beliefs are held by an idealized rational agent, denoted by You. This ‘You’ can be a human, but also a robot, a computer program...

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Book 1998ld and must therefore be addressed by every system that attempts to represent reality. The representation of uncertainty is a ma­ jor concern of philosophers, logicians, artificial intelligence researchers and com­ puter sciencists, psychologists, statisticians, economists and engineers. The present

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Handbook of Defeasible Reasoning and Uncertainty Management Systemshttp://image.papertrans.cn/q/image/780745.jpg

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https://doi.org/10.1007/978-94-017-1735-9fuzzy; fuzzy logic; statistical inference; uncertainty

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Probability, Possibility, Belief: Which and Where?, present some models for the quantified representation of uncertainty, focusing on their applicability more than on their mathematical structure. Imprecision often underlies uncertainty, hence we will also study imprecision.

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Statistical Inference,specify a probability distribution of possible outcomes. The actual occurrence of a particular outcome will intuitively seem to support one or other of the hypotheses which might have generated it. Statistical inference attempts to discover the rules by which such support may be gauged.

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The Evidentiary Value Model,Theories of evidence and theories of knowledge are intimately linked together. And there are many competing theories of evidence. One way to approach them is by way of looking at the theories of knowledge which are their bedrock.