Comparer des méthodes

	Simulation Bootstrap	Inférence bayésienne
Domaine	Simulation	Statistique
Famille	Process / pipeline	Bayesian methods
Année d'origine	1979	1763
Auteur d'origine	Bradley Efron	Thomas Bayes; Pierre-Simon Laplace
Type	Simulation-based nonparametric inference	Probabilistic inference paradigm
Source fondatrice	Efron, B. & Tibshirani, R.J. (1993). An Introduction to the Bootstrap. Chapman & Hall/CRC.	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.
Alias	bootstrap resampling, empirical resampling, nonparametric bootstrap, Önyükleme Simülasyonu (Bootstrap Resampling)	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Apparentées	5	3
Résumé	Bootstrap simulation, introduced by Bradley Efron in 1979, is a simulation-based inference method that derives the sampling distribution of virtually any statistic by repeatedly resampling with replacement from the observed data. Because it requires no parametric distributional assumptions, it provides a robust, general-purpose alternative to analytical confidence intervals and parametric hypothesis tests across continuous, ordinal, binary, and count data.	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.