Porównaj metody

	Bayesowska wielokrotna regresja liniowa	Bayesowski Model Liniowy Uogólniony
Dziedzina	Statystyka	Statystyka
Rodzina	Regression model	Regression model
Rok powstania	1971	1989 (GLM); 1995 (Bayesian BDA)
Twórca	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Typ	Bayesian parametric regression	Bayesian regression model
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	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
Inne nazwy	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Pokrewne	6	6
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.	A Bayesian Generalized Linear Model (Bayesian GLM) extends the classical GLM framework by placing prior distributions on the regression coefficients and updating them with data via Bayes' theorem. This yields a full posterior distribution over parameters rather than single point estimates, enabling richer uncertainty quantification and principled incorporation of prior knowledge for any exponential-family outcome.