The mean value of a fuzzy number
Fuzzy Sets and Systems - Fuzzy Numbers
Graded sets and points: a stratified approach to fuzzy sets and points
Fuzzy Sets and Systems
The three semantics of fuzzy sets
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
On the variance of fuzzy random variables
Fuzzy Sets and Systems
Supremum preserving upper probabilities
Information Sciences: an International Journal
The necessity of the strong &agr;-cuts of a fuzzy set
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - Special issue on aggregation operators
The variance and covariance of fuzzy random variables and their applications
Fuzzy Sets and Systems
Imprecise distribution function associated to a random set
Information Sciences—Informatics and Computer Science: An International Journal
Joint propagation of probability and possibility in risk analysis: Towards a formal framework
International Journal of Approximate Reasoning
A behavioural model for vague probability assessments
Fuzzy Sets and Systems
Random intervals as a model for imprecise information
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Advocating the Use of Imprecisely Observed Data in Genetic Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Representing parametric probabilistic models tainted with imprecision
Fuzzy Sets and Systems
International Journal of Approximate Reasoning
Genetic learning of fuzzy rules based on low quality data
Fuzzy Sets and Systems
On the variability of the concept of variance for fuzzy random variables
IEEE Transactions on Fuzzy Systems
GFS-based analysis of vague databases in high performance athletics
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Diagnosis of dyslexia with low quality data with genetic fuzzy systems
International Journal of Approximate Reasoning
Upper and lower probabilities induced by a fuzzy random variable
Fuzzy Sets and Systems
Upper and lower probabilities induced by a fuzzy random variable
Fuzzy Sets and Systems
Mark-recapture techniques in statistical tests for imprecise data
International Journal of Approximate Reasoning
Linguistic cost-sensitive learning of genetic fuzzy classifiers for imprecise data
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Gradualness, uncertainty and bipolarity: Making sense of fuzzy sets
Fuzzy Sets and Systems
Analysing the low quality of the data in lighting control systems
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Expert Systems with Applications: An International Journal
Fuzzy set-valued stochastic Lebesgue integral
Fuzzy Sets and Systems
Comparison of fuzzy functions for low quality data GAP algorithms
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Inner and outer fuzzy approximations of confidence intervals
Fuzzy Sets and Systems
Rough Sets, Coverings and Incomplete Information
Fundamenta Informaticae - Advances in Rough Set Theory
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A fuzzy random variable is viewed as the imprecise observation of the outcomes in a random experiment. Since randomness and vagueness coexist in the same framework, it seems reasonable to integrate fuzzy random variables into imprecise probabilities theory. Nevertheless, fuzzy random variables are commonly presented in the literature as classical measurable functions associated to a classical probability measure. We present here a higher order possibility model that represents the imprecise information provided by a fuzzy random variable. We compare it with previous classical models in the literature. First, some aspects about the acceptability function associated to a fuzzy random variable are investigated. Secondly, we present three different higher order possibility models, all of them arising in a natural way. We investigate their similarities and differences, and observe that the first one (the fuzzy probability envelope) is the most informative. Finally we compare the fuzzy probability envelope with the (classical) probability measure induced by the fuzzy random variable. We conclude that the classical probability measure does not always contain all relevant information provided by a fuzzy random variable.