Comparer des méthodes

	Régression linéaire bayésienne	Régression par Moindres Carrés Ordinaires (MCO)
Domaine	Bayésien	Économétrie
Famille	Bayesian methods	Regression model
Année d'origine	2013 (modern reference); foundations 18th–19th century	2019
Auteur d'origine	Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.	Wooldridge (textbook treatment); classical least squares
Type	Bayesian linear model	Linear regression
Source fondatrice	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Alias	bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Apparentées	4	5
Résumé	Bayesian linear regression is a probabilistic extension of the ordinary linear model, introduced through Bayes' rule and formalised in its modern computational workflow by Gelman et al. (2013). Rather than returning a single point estimate for each coefficient, it combines a user-specified prior distribution with the likelihood of the observed data to produce a full posterior distribution over all parameters, from which credible intervals and posterior predictive distributions are derived.	Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE).