Methoden vergleichen

	Bayesianische Inferenz	Möglichkeitstheorie
Fachgebiet	Statistik	Soft Computing
Familie	Bayesian methods	Machine learning
Entstehungsjahr	1763	1988
Urheber	Thomas Bayes; Pierre-Simon Laplace	Lotfi Zadeh; Didier Dubois & Henri Prade
Typ	Probabilistic inference paradigm	Uncertainty quantification framework
Wegweisende Quelle	Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.	Dubois, D., & Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press. ISBN: 978-0-306-42520-2
Aliasnamen	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference	Fuzzy Possibility Theory, Possibilistic Reasoning, Olasılık Teorisi (Bulanık), Possibility Distribution Theory
Verwandt	3	3
Zusammenfassung	Bayesian inference is a statistical paradigm in which probability represents degrees of belief rather than long-run frequencies. It encodes prior knowledge about parameters in a prior distribution, combines that prior with the likelihood of observed data via Bayes' theorem, and produces a posterior distribution that quantifies updated uncertainty. The foundational theorem was published posthumously by Thomas Bayes in 1763 and subsequently systematized by Pierre-Simon Laplace in his 1812 Théorie analytique des probabilités.	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.