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

GONG

Confirmation Theory,otion of confirmation. . of . given . is written .(.,.). Strictly speaking, the evidence . will be in addition to some background knowledge .. Thus we should really write .(., .). The background knowledge will, however, often be omitted for ease of writing, but it should not be forgotten.

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Possibility Theory: Qualitative and Quantitative Aspects,idence in an event is not totally determined by the confidence in the opposite event, as assumed in probability theory. Possibility theory belongs to this trend that describes partial belief in terms of certainty and plausibility, viewed as distinct concepts. The distinctive features of possibility

招人嫉妒

Families of Probabilities,ability of the hypothesis . can be regarded neither as greater than that of hypothesis ., nor less than that of hypothesis .,nor yet equal to that of hypothesis .. Although he did not provide a mathematical structure for his probability values, he did give us some hints: Every probability is compara

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The Transferable Belief Model for Quantified Belief Representation,as the Bayesian model, i.e., the graded dispositions that guide ‘our’ behaviour. We use the word ‘belief’ in a broad sense. It could be replaced by quantified credibility, subjective support, strength of opinion... These beliefs are not categorical as in modal logic, but admits degrees as in probabi

Affable

Infinitesimal Theories of Uncertainty for Plausible Reasoning,wn this way are just plausible and can be revised in the light of new information. This kind of reasoning is called . or . reasoning (e.g. [Reiter, 1980]). In this chapter, we are interested in how uncertainty models such as probability theory, possibility theory [Zadeh, 1978; Dubois and Prade, 1988

Conflagration

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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Intentional

Psychological Challenges for Normative Models,llowing dictum: ‘Look, either reasoning is rational or it’s psychological’. In this chapter, I argue against the widespread view that the rational and the psychological are opposed. According to this view, the rational is defined by the laws of probability and logic — that is, by content-free axioms

MINT

e real world 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. T

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