Possibility theory
By Didier Dubois, Henri Prade. Scholarpedia, 2007
834 citations recorded by the source corpus.
Subfields: Atomic and Molecular Physics, and Optics, Experimental and Cognitive Psychology, Philosophy
View Paper: https://www.semanticscholar.org/paper/e02e32a26930a13ce9a877d76fee7669b2e185d5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atomic and Molecular Physics, and Optics
- M. Taheri
- Tomasa Calvo
- Michael Beer
- Edmundas Kazimieras Zavadskas
- Ryan Martin
- Henry Leung
- Vladik Kreinovich
- S. A. Torabi
In Experimental and Cognitive Psychology
- M. Taheri
- Michael Beer
- Edmundas Kazimieras Zavadskas
- Ryan Martin
- Henry Leung
- Vladik Kreinovich
- S. A. Torabi
- Eyke Hüllermeier
In Philosophy
- M. Taheri
- Michael Beer
- Ryan Martin
- Henry Leung
- Vladik Kreinovich
- S. A. Torabi
- Thomas Lukasiewicz
- Eyke Hüllermeier
Citations in the corpus
- Managing uncertainty and vagueness in description logics for the Semantic Web (2008, background): “A standard example that points out the difference between degrees of uncertainty and degrees of truth is that of a bottle [26].”
- Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty (2014, methodology): “To circumvent this limitation, we refer to the more flexible framework of possibility theory, in which uncertainty is modeled in terms of two measures instead of a single one [9].”
- Robust possibilistic programming for socially responsible supply chain network design: A new approach (2012, methodology): “In the past years, by relying on the theory of possibility [33,34], a number of methods have been developed to cope with imprecise coefficients in both objective functions and constraints.”
- Data fusion in intelligent transportation systems: Progress and challenges - A survey (2011, methodology): “– Probabilistic approaches: for instance, Bayesian approach with Bayesian network and state-space models [10], maximum likelihood methods and Kalman filter based DF [11,12], possibility theory [13], evidential reasoning and more specifically evidence theory [14–16] are widely used for the multi-sens”
- Is there a need for fuzzy logic? (2008, background): “An important branch of FLe is possibility theory [85,86,89,15].”
- Aggregation Functions: A Guide for Practitioners (2007, background): “Possibility and necessity measures are the basis of the theory of possibility [85, 252, 284].”
- Fuzzy p-value in testing fuzzy hypotheses with crisp data (2008, methodology): “There are many ways to carry out this comparison, see, for example, Dubois and Prade (1988); Wang and Kerre (2001), and Denœux et al. (2005).”
- An imprecise-probabilistic characterization of frequentist statistical inference (2021, background): “, Miranda 2008; Troffaes and de Cooman 2014; Walley 1991) be valid? Another seemingly innocuous restriction is the requirement that (4) hold for all α ∈ [0, 1]. This implies that (x,A) 7→ Πx(A) can and will take values arbitrarily close to 1. It seems to me that this more or less implies that Πx mus”
- n-MONOTONE EXACT FUNCTIONALS (2008, background): “Coherent lower previsions can be viewed as lower expectations with respect to closed convex sets of probability measures (also called credal sets; see L evi [13]), and they provide a unifying framework for studying many other uncertainty models , such as probability charges (Bhaskara Rao and Bhaskar”
- Stochastic and epistemic uncertainty propagation in LCA (2013, background): “While the function presented in Figure 1 is a trapezoidal distribution, more complex distributions can be adopted to suit available information (See Dubois and Prade 1988).”
- A new fuzzy additive ratio assessment method (ARAS‐F). Case study: The analysis of fuzzy multiple criteria in order to select the logistic centers location (2010, background): “The most commonly used membership functions are the following (Dubois and Prade 1988): triangular, trapezoid, linear, sigmoidal, π-type, and Gaussian.”
- Imprecise probabilities in engineering analyses (2013, positive): “These ideas underpin generalizations to possibility distributions [41], fuzzy probability [42], and info-gap decision theory [15].”
- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods (2019, background): “Correspondingly, various generalizations of standard probability theory have been proposed, including imprecise probability (Walley, 1991), evidence theory (Shafer, 1976; Smets and Kennes, 1994), and possibility theory (Dubois and Prade, 1988).”