Vertaile menetelmiä

	Bayesiläinen yksinkertainen lineaarinen regressio	Bayesilainen yleistetty lineaarinen malli
Tieteenala	Tilastotiede	Tilastotiede
Menetelmäperhe	Regression model	Regression model
Syntyvuosi	Early 19th century; textbook synthesis 2013	1989 (GLM); 1995 (Bayesian BDA)
Kehittäjä	Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al.	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
Tyyppi	Bayesian linear regression	Bayesian regression model
Alkuperäislähde	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
Rinnakkaisnimet	Bayesian SLR, Bayesian univariate regression, probabilistic simple linear regression, Bayesian linear model	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
Liittyvät	6	6
Tiivistelmä	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.	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.