Compara mètodes

	Regressió Lineal Simple Bayesiana	Regressió Múltiple Bayesiana
Camp	Estadística	Estadística
Família	Regression model	Regression model
Any d'origen	Early 19th century; textbook synthesis 2013	1971
Autor original	Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al.	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.
Tipus	Bayesian linear regression	Bayesian parametric regression
Font seminal	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
Àlies	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
Relacionats	6	6
Resum	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.