방법 비교

	베이지안 일반화 가법 모형(Bayesian Generalized Additive Model, Bayesian GAM)	베이즈 일반화 선형 모형
분야	통계학	통계학
계열	Regression model	Regression model
기원 연도	1990s–2000s	1989 (GLM); 1995 (Bayesian BDA)
창시자	Hastie & Tibshirani (GAM framework, 1990); Bayesian formulation developed through work by Wood, Fahrmeir, Lang, and others	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
유형	Semiparametric Bayesian regression	Bayesian regression model
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 GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
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.	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.