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The paper introduces the main measures of uncertainty and discusses some of the problems associated with conventional uncertainty propagation methods, such as those based on independent probabilities and the methods used with certainty factors, belief, and possibility functions. Some examples show the importance of the associated errors. Alternative methods, such as log‐linear regression and causal networks or influence diagram models, are discussed. Finally, their structural and parametric learning possibilities are analyzed.

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