Сравнение методов

	Байесовское тестирование моделей	Байесовский вывод
Область	Дизайн исследования	Статистика
Семейство	Process / pipeline	Bayesian methods
Год появления	1935 (Jeffreys); widely adopted in social and behavioral sciences from the 1990s onward	1763
Автор метода	Harold Jeffreys; formalized for applied sciences by Robert Kass and Adrian Raftery	Thomas Bayes; Pierre-Simon Laplace
Тип	Quantitative inferential research design	Probabilistic inference paradigm
Основополагающий источник	Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795.	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.
Другие названия	Bayesian hypothesis testing, Bayesian model comparison, Bayes factor analysis, BMT	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Связанные	4	3
Сводка	Bayesian model testing research is a quantitative design in which competing theoretical models or hypotheses are evaluated by comparing their marginal likelihoods given observed data. The central tool is the Bayes factor — a ratio that quantifies how much more likely the data are under one model than under another. Unlike null-hypothesis significance testing, Bayesian model testing yields direct evidence for or against specific hypotheses, incorporates prior knowledge, and can support a null hypothesis rather than merely failing to reject it.	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.