When upper probabilities are possibility measures
Fuzzy Sets and Systems - Special issue dedicated to Professor Claude Ponsard
Evidence, knowledge, and belief functions
International Journal of Approximate Reasoning - Special issue: The belief functions revisited: questions and answers
Measures of uncertainty in expert systems
Artificial Intelligence
Algorithms for Conditioning on Events of Zero Lower Probability
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Decision making under uncertainty using imprecise probabilities
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Some properties of a random set approximation to upper and lower distribution functions
International Journal of Approximate Reasoning
A survey of the theory of coherent lower previsions
International Journal of Approximate Reasoning
Unifying practical uncertainty representations -- I: Generalized p-boxes
International Journal of Approximate Reasoning
Unifying practical uncertainty representations. II: Clouds
International Journal of Approximate Reasoning
Finite approximations to coherent choice
International Journal of Approximate Reasoning
Practical representations of incomplete probabilistic knowledge
Computational Statistics & Data Analysis
Utilizing belief functions for the estimation of future climate change
International Journal of Approximate Reasoning
Imprecise expectations for imprecise linear filtering
International Journal of Approximate Reasoning
Regression analysis using the imprecise Bayesian normal model
International Journal of Data Analysis Techniques and Strategies
Reliability sensitivity analysis for structural systems in interval probability form
Structural and Multidisciplinary Optimization
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Given an imprecise probabilistic model over a continuous space, computing lower/upper expectations is often computationally hard to achieve, even in simple cases. Because expectations are essential in decision making and risk analysis, tractable methods to compute them are crucial in many applications involving imprecise probabilistic models. We concentrate on p-boxes (a simple and popular model), and on the computation of lower expectations of non-monotone functions. This paper is devoted to the univariate case, that is where only one variable has uncertainty. We propose and compare two approaches: the first using general linear programming, and the second using the fact that p-boxes are special cases of random sets. We underline the complementarity of both approaches, as well as the differences.