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	Bayesowska wielokrotna regresja liniowa	Regresja metodą najmniejszych kwadratów (OLS)
Dziedzina	Statystyka	Ekonometria
Rodzina	Regression model	Regression model
Rok powstania	1971	2019
Twórca	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.	Wooldridge (textbook treatment); classical least squares
Typ	Bayesian parametric regression	Linear regression
Źródło pierwotne	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
Inne nazwy	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Pokrewne	6	5
Podsumowanie	Bayesian Multiple Linear Regression models a continuous outcome as a linear combination of several predictors, but instead of producing a single point estimate it yields a full posterior distribution over all regression coefficients and the error variance. This makes uncertainty quantification explicit and allows seamlessly incorporating prior knowledge from theory or previous studies.	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).