Possibility theory and statistical reasoning
By Didier Dubois. Computational Statistics & Data Analysis, 2006
477 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/ba442bd7c7ed09847106e54bc90b7579e1811176
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Enrico Zio
- Willem Waegeman
- Ryan Martin
- Elisa Ferrario
- Nicola Pedroni
- M. Couplet
- Alberto Pasanisi
- Eyke Hüllermeier
In Management Science and Operations Research
- Enrico Zio
- Ryan Martin
- Elisa Ferrario
- Nicola Pedroni
- M. Couplet
- Alberto Pasanisi
- Willem Waegeman
- Eyke Hüllermeier
In Statistics and Probability
- Enrico Zio
- Ryan Martin
- Elisa Ferrario
- Nicola Pedroni
- M. Couplet
- Alberto Pasanisi
- Eyke Hüllermeier
- Willem Waegeman
Citations in the corpus
- The future of risk assessment (2018, background): “Note that the formulation in (3) does not restrict the representation of the uncertainty to the classical probabilistic one and alternative representations can be employed [18,22,57,70,150].”
- The role of fuzzy sets in decision sciences: Old techniques and new directions (2011, methodology): “Then we consider the use of membership grades and linguistic terms for rating the worth of decisions.”
- Hierarchical propagation of probabilistic and non-probabilistic uncertainty in the parameters of a risk model (2013, methodology): “, fuzzy set theory [8], DempsterShafer theory of evidence [9]-[14], possibility theory [15]-[18] and interval analysis [19]-[21].”
- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods (2019, background): “Thus, Π is “maxitive” instead of additive (Shilkret, 1971; Dubois, 2006).”
- An imprecise-probabilistic characterization of frequentist statistical inference (2021, background): “A plot of this contour is also displayed in Figure 5(b) and, since I used the same data as in Schweder and Hjort (2013), in particular, X = (1.333, 0.333), my plot is the same as in their Figure 1.3 Here, again, note the gain in efficiency between doing the naive marginalization of the joint plausib”
- Stochastic and epistemic uncertainty propagation in LCA (2013, background): “As suggested by Dubois and Guyonnet (2011), this may prove impractical in a decisionmaking framework.”