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	Bejzijanska jednostavna linearna regresija	Bejzijanska višestruka linearna regresija
Oblast	Statistika	Statistika
Porodica	Regression model	Regression model
Godina nastanka	Early 19th century; textbook synthesis 2013	1971
Tvorac	Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al.	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.
Tip	Bayesian linear regression	Bayesian parametric regression
Temeljni izvor	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
Drugi nazivi	Bayesian SLR, Bayesian univariate regression, probabilistic simple linear regression, Bayesian linear model	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression
Srodne	6	6
Sažetak	Bayesian Simple Linear Regression models the relationship between a continuous outcome and a single predictor by combining a Gaussian likelihood with prior distributions over the intercept, slope, and error variance. The result is a full posterior distribution over all parameters, providing probabilistic uncertainty quantification rather than a single point estimate.	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.