Comparer des méthodes

	Théorie de Dempster-Shafer des évidences	Probabilité Imprécise
Domaine	Soft computing	Soft computing
Famille	Machine learning	Bayesian methods
Année d'origine	1976	1991
Auteur d'origine	Arthur P. Dempster & Glenn Shafer	Peter Walley
Type	Uncertainty calculus for combining evidence	Set-valued probability model
Source fondatrice	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.	Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman & Hall. ISBN: 978-0-412-28660-5
Alias	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	Lower-Upper Probability, Robust Bayesian Analysis, Credal Set Theory, Belirsiz Olasılık
Apparentées	4	3
Résumé	Dempster-Shafer theory is a mathematical framework for reasoning under uncertainty that generalizes Bayesian probability by representing ignorance explicitly. Instead of forcing a single probability on each hypothesis, it assigns belief mass to sets of hypotheses and derives a belief-plausibility interval, and it provides Dempster's rule for fusing evidence from multiple independent sources. Developed from Arthur Dempster's 1967 work and Glenn Shafer's 1976 monograph, it underpins evidential reasoning and sensor/decision fusion.	Imprecise probability is a generalization of standard probability theory that represents epistemic uncertainty through sets of probability measures, called credal sets, rather than a single precise distribution. Introduced systematically by Peter Walley in his 1991 monograph, the framework characterizes beliefs via lower and upper probabilities (or previsions), bracketing the range of plausible probability assignments when available information is insufficient to determine a unique measure.