
Presenting convex sets of probability distributions by convex semilattices and unique bases
We prove that every finitely generated convex set of finitely supported ...
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Computing Probability Intervals Under Independency Constraints
Many AI researchers argue that probability theory is only capable of dea...
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Revision of ISO 19229 to support the certification of calibration gases for purity
The second edition of ISO 19229 expands the guidance in its predecessor ...
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Combination of Upper and Lower Probabilities
In this paper, we consider several types of information and methods of c...
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Formal Model of Uncertainty for Possibilistic Rules
Given a universe of discourse Xa domain of possible outcomesan experim...
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Some Problems for Convex Bayesians
We discuss problems for convex Bayesian decision making and uncertainty ...
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Sufficiency, Separability and Temporal Probabilistic Models
Suppose we are given the conditional probability of one variable given s...
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Calculating Uncertainty Intervals From Conditional Convex Sets of Probabilities
In Moral, Campos (1991) and Cano, Moral, VerdegayLopez (1991) a new method of conditioning convex sets of probabilities has been proposed. The result of it is a convex set of nonnecessarily normalized probability distributions. The normalizing factor of each probability distribution is interpreted as the possibility assigned to it by the conditioning information. From this, it is deduced that the natural value for the conditional probability of an event is a possibility distribution. The aim of this paper is to study methods of transforming this possibility distribution into a probability (or uncertainty) interval. These methods will be based on the use of Sugeno and Choquet integrals. Their behaviour will be compared in basis to some selected examples.
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