Сравнение методов

	Байесовский вывод	Теория Демпстера
Область	Статистика	Мягкие вычисления
Семейство	Bayesian methods	Machine learning
Год появления	1763	1976
Автор метода	Thomas Bayes; Pierre-Simon Laplace	Arthur P. Dempster & Glenn Shafer
Тип	Probabilistic inference paradigm	Uncertainty calculus for combining evidence
Основополагающий источник	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.	Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 38(2), 325–339.
Другие названия	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference	evidence theory, belief functions, evidential reasoning, Dempster-Shafer kanıt teorisi
Связанные	3	4
Сводка	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.	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.