Bandingkan kaedah

	Teori Kebarangkalian	Teori Bukti Dempster-Shafer	Pengkomputan Berbutir (Granulasi Maklumat)	Kebarangkalian Tidak Tepat
Bidang	Perkomputeran Lembut	Perkomputeran Lembut	Perkomputeran Lembut	Perkomputeran Lembut
Keluarga	Machine learning	Machine learning	Machine learning	Bayesian methods
Tahun asal	1988	1976	1997	1991
Pengasas	Lotfi Zadeh; Didier Dubois & Henri Prade	Arthur P. Dempster & Glenn Shafer	Lotfi A. Zadeh (information granulation); developed by Pedrycz, Skowron, Yao	Peter Walley
Jenis	Uncertainty quantification framework	Uncertainty calculus for combining evidence	Framework for multi-granularity information processing	Set-valued probability model
Sumber perintis	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.	Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127.	Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman & Hall. ISBN: 978-0-412-28660-5
Alias	Fuzzy Possibility Theory, Possibilistic Reasoning, Olasılık Teorisi (Bulanık), Possibility Distribution Theory	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi	information granulation, computing with granules, three-way granular computing, tanecikli hesaplama	Lower-Upper Probability, Robust Bayesian Analysis, Credal Set Theory, Belirsiz Olasılık
Berkaitan	3	4	3	3
Ringkasan	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.	Granular computing is a problem-solving paradigm that processes information in 'granules' — clumps of objects drawn together by indistinguishability, similarity, or functionality — rather than at the level of individual data points. Articulated by Lotfi Zadeh in 1997 as fuzzy information granulation and developed into a broad framework, it provides a unifying umbrella over fuzzy sets, rough sets, and interval methods, letting analysis move to whichever level of detail a problem actually requires.	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.