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| Régression bayésienne× | Régression par Moindres Carrés Ordinaires (MCO)× | |
|---|---|---|
| Domaine≠ | Bayésien | Économétrie |
| Famille≠ | Bayesian methods | Regression model |
| Année d'origine≠ | — | 2019 |
| Auteur d'origine≠ | — | 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 regression, probabilistic regression, bayesian regresyon | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Apparentées≠ | 2 | 5 |
| Résumé≠ | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. | 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). |
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