विधियों की तुलना करें

	डेम्पस्टर-शेफ़र साक्ष्य का सिद्धांत	संभावना सिद्धांत
क्षेत्र	सॉफ्ट कंप्यूटिंग	सॉफ्ट कंप्यूटिंग
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1976	1988
प्रवर्तक	Arthur P. Dempster & Glenn Shafer	Lotfi Zadeh; Didier Dubois & Henri Prade
प्रकार	Uncertainty calculus for combining evidence	Uncertainty quantification framework
मौलिक स्रोत	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
उपनाम	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	Fuzzy Possibility Theory, Possibilistic Reasoning, Olasılık Teorisi (Bulanık), Possibility Distribution Theory
संबंधित	4	3
सारांश	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.