방법 비교

	베이지안 일반화 가법 모형(Bayesian Generalized Additive Model, Bayesian GAM)	베이즈 다중 선형 회귀
분야	통계학	통계학
계열	Regression model	Regression model
기원 연도	1990s–2000s	1971
창시자	Hastie & Tibshirani (GAM framework, 1990); Bayesian formulation developed through work by Wood, Fahrmeir, Lang, and others	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.
유형	Semiparametric Bayesian regression	Bayesian parametric regression
원전	Wood, S. N. (2017). Generalized Additive Models: An Introduction with R (2nd ed.). CRC Press. ISBN: 9781498728331	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
별칭	Bayesian GAM, BGAM, Bayesian semiparametric regression, Bayesian smooth regression	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression
관련	4	6
요약	Bayesian Generalized Additive Models extend the frequentist GAM framework by placing prior distributions over the smooth functions and any additional model parameters. This yields full posterior distributions over each smooth effect, enabling principled uncertainty quantification, automatic smoothness selection via hyperpriors, and seamless integration with hierarchical or mixed-effects structures.	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.