Comparar métodos

	Probabilidad Imprecisa	Teoría de la Evidencia Dempster-Shafer
Campo	Computación blanda	Computación blanda
Familia	Bayesian methods	Machine learning
Año de origen	1991	1976
Autor original	Peter Walley	Arthur P. Dempster & Glenn Shafer
Tipo	Set-valued probability model	Uncertainty calculus for combining evidence
Fuente seminal	Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman & Hall. ISBN: 978-0-412-28660-5	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.
Alias	Lower-Upper Probability, Robust Bayesian Analysis, Credal Set Theory, Belirsiz Olasılık	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi
Relacionados	3	4
Resumen	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.	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.