Compară metode

	Teoria Posibilității	Probabilitate Imprecisă
Domeniu	Soft computing	Soft computing
Familie	Machine learning	Bayesian methods
Anul apariției	1988	1991
Autorul original	Lotfi Zadeh; Didier Dubois & Henri Prade	Peter Walley
Tip	Uncertainty quantification framework	Set-valued probability model
Sursa seminală	Dubois, D., & Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press. ISBN: 978-0-306-42520-2	Walley, P. (1991). Statistical Reasoning with Imprecise Probabilities. Chapman & Hall. ISBN: 978-0-412-28660-5
Denumiri alternative	Fuzzy Possibility Theory, Possibilistic Reasoning, Olasılık Teorisi (Bulanık), Possibility Distribution Theory	Lower-Upper Probability, Robust Bayesian Analysis, Credal Set Theory, Belirsiz Olasılık
Înrudite	3	3
Rezumat	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.	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.