विधियों की तुलना करें

	संभावना सिद्धांत	डेम्पस्टर-शेफ़र साक्ष्य का सिद्धांत
क्षेत्र	सॉफ्ट कंप्यूटिंग	सॉफ्ट कंप्यूटिंग
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1988	1976
प्रवर्तक	Lotfi Zadeh; Didier Dubois & Henri Prade	Arthur P. Dempster & Glenn Shafer
प्रकार	Uncertainty quantification framework	Uncertainty calculus for combining evidence
मौलिक स्रोत	Dubois, D., & Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press. ISBN: 978-0-306-42520-2	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.
उपनाम	Fuzzy Possibility Theory, Possibilistic Reasoning, Olasılık Teorisi (Bulanık), Possibility Distribution Theory	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi
संबंधित	3	4
सारांश	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.	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.