Comparar métodos

	Teoria da Evidência de Dempster-Shafer	Teoria da Possibilidade
Área	Soft computing	Soft computing
Família	Machine learning	Machine learning
Ano de origem	1976	1988
Autor original	Arthur P. Dempster & Glenn Shafer	Lotfi Zadeh; Didier Dubois & Henri Prade
Tipo	Uncertainty calculus for combining evidence	Uncertainty quantification framework
Fonte seminal	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.	Dubois, D., & Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press. ISBN: 978-0-306-42520-2
Outros nomes	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	Fuzzy Possibility Theory, Possibilistic Reasoning, Olasılık Teorisi (Bulanık), Possibility Distribution Theory
Relacionados	4	3
Resumo	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.	Possibility Theory is a mathematical framework for representing and reasoning under uncertainty, introduced by Lotfi Zadeh in 1978 and systematically developed by Didier Dubois and Henri Prade in their 1988 monograph. It uses possibility distributions — functions assigning a degree in [0,1] to each element of a universe — to encode what is plausible or consistent with available information, complementing probability theory for situations where data is scarce or knowledge is imprecise.