Vertaile menetelmiä

	Dempster-Shafer-teoria todistusaineistosta	Mahdollisuusteoria
Tieteenala	Pehmeä laskenta	Pehmeä laskenta
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1976	1988
Kehittäjä	Arthur P. Dempster & Glenn Shafer	Lotfi Zadeh; Didier Dubois & Henri Prade
Tyyppi	Uncertainty calculus for combining evidence	Uncertainty quantification framework
Alkuperäislähde	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
Rinnakkaisnimet	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	Fuzzy Possibility Theory, Possibilistic Reasoning, Olasılık Teorisi (Bulanık), Possibility Distribution Theory
Liittyvät	4	3
Tiivistelmä	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.