Krahasoni metodat

	Regresioni Lineor i Thjeshtë Bayesian	Regresioni Multilinear Bayesiano
Fusha	Statistikë	Statistikë
Familja	Regression model	Regression model
Viti i origjinës	Early 19th century; textbook synthesis 2013	1971
Krijuesi	Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al.	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.
Lloji	Bayesian linear regression	Bayesian parametric regression
Burimi themelues	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
Emërtime të tjera	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
Të lidhura	6	6
Përmbledhja	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.