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Constructing imprecise probability distributions
International Journal of General Systems, 2005In this current paper the following problems are addressed: (1) extending the knowledge of a partially known probability distribution function to any point of a continuous sample space, (2) constructing an imprecise probability distribution based on the knowledge of a set of credible or confidence intervals, and (3) computing the lower and upper ...
Igor Kozine, Lev V. Utkin
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Imprecise quantifiers and conditional probabilities
1991Expert rules used in knowledge-based systems are often pervaded with uncertainty and subject to exceptions. Numerical quantifiers are a natural way of expressing the proportion of exceptions or the probability of encountering them. The available knowledge about the proportion of A's being B's, or more generally the probability P(BIA) for an A to be a B,
Stéphane Amarger +2 more
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From imprecise to granular probabilities
Fuzzy Sets and Systems, 2005The author discusses G. de Cooman's work on imprecise probabilities. First he points out that imprecise probabilities in de Cooman's sense are a special case of granular probabilities in the author's sense. Second, the class of imprecise probabilities in de Cooman's sense is not closed under imprecisely defined operations, i.e. if initial probabilities
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Imprecise probability and expert forecasting
Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94, 2002Evidence is often insufficient to support the assessment of precise probabilities. Shifting to vaguer measures of uncertainty, such as upper and lower probabilities, does not deprive one of the key analytical tools of classical probability. Two approaches to the calculation of upper and lower expected values are described and contrasted in the case of ...
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Event‐Tree Analysis with Imprecise Probabilities
Risk Analysis, 2011Novel methods are proposed for dealing with event‐tree analysis under imprecise probabilities, where one could measure chance or uncertainty without sharp numerical probabilities and express available evidence as upper and lower previsions (or expectations) of gambles (or bounded real functions). Sets of upper and lower previsions generate a convex set
You, Xiaomin, Tonon, Fulvio
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Decision Making with Imprecise Probabilities
1994In many decision problems the only information available about a random event is expert opinion. The theory of imprecise probabilities, a generalization of standard subjective probability, allows us to deal with such information. In this paper the use of imprecise probabilities is discussed, with emphasis on elicitation and combination of opinions and ...
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Probability Inequalities with Imprecise Previsions
We investigate how various well known probability inequalities extend to lower and upper previsions. Our focus is especially on Markov’s, Bhatia-Davis, Jensen’s and Cantelli’s inequalities. In all such cases, imprecise versions of these inequalities are available even requiring the weak consistency notion of 2-coherence, which implies that they obtain ...Pelessoni, Renato, Vicig, Paolo
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Bagging of credal decision trees for imprecise classification
Expert Systems With Applications, 2020Javier G Castellano +2 more
exaly
Imprecise and Indeterminate Probabilities
2017This chapter offers a discussion of imprecision and indeterminacy in probability values; that is, there is a focus on situations where one does not attach a single real number to every possible event. There are several theories and mathematical models regarding such imprecise and indeterminate probabilities.
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Discounting Imprecise Probabilities
2018In this paper it is considered the problem of discounting a credal set of probability distributions by a factor \(\alpha \) representing a degree of unreliability of the information source providing the imprecise probabilistic information. An axiomatic approach is followed by giving a set of properties that this operator should satisfy.
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