Expert Systems · 2017 · 18 citations · 13 references
Natural SciencesMathematicsProbability TheorySet TheoryUncertainty QuantificationFuzzy LogicProbabilistic ModelingProbability DistributionsProbability In ScienceUncertainty EstimationUncertainty RepresentationEpistemic UncertaintyFuzzy NumbersFuzzy SetsMembership FunctionsProbabilistic Uncertainty Analysis
Abstract Uncertainty comes in many forms in the real world and is an unavoidable component of human life. Generally, two types of uncertainties arise, namely, aleatory and epistemic uncertainty. Probability is a well established mathematical tool to handle aleatory uncertainty and fuzzy set theory is a tool to handle epistemic uncertainty. However, in certain situations, parameters of probability distributions may be tainted with epistemic uncertainty; and so, representation of parameters of probability distributions may be treated as fuzzy numbers (may be of different shapes). A probability box (P‐box) can be constructed when parameters are not precisely known. In this paper, an attempt has been made to construct families of P‐boxes when parameters of probability distributions are bell shaped or normal fuzzy numbers; and from these families of P‐boxes, membership functions are generated at different fractiles for different alpha levels.
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Elementary Numerical Analysis: An Algorithmic Approach
J. W. W., S. D. Conte · Mathematics of Computation · 1967 · 1.3K citations