방법 비교

	베이즈 일반화 선형 모형	베이지안 로지스틱 회귀
분야	통계학	베이지안
계열	Regression model	Bayesian methods
기원 연도	1989 (GLM); 1995 (Bayesian BDA)	2008
창시자	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.	Gelman, Jakulin, Pittau & Su (weakly-informative prior framework, 2008)
유형	Bayesian regression model	Bayesian classification model
원전	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., Jakulin, A., Pittau, M. G. & Su, Y.-S. (2008). A Weakly Informative Default Prior Distribution for Logistic and Other Regression Models. Annals of Applied Statistics, 2(4), 1360–1383.
별칭	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM	bayesian binary logistic regression, bayesian classification model, Bayesian Lojistik Regresyon
관련	6	3
요약	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.	Bayesian logistic regression is a classification model that applies Bayesian inference to a logistic (sigmoid) likelihood for binary or multinomial outcomes. Developed within the weakly-informative prior framework formalised by Gelman, Jakulin, Pittau and Su (2008), it places a prior distribution over the coefficients and combines that prior with the data likelihood to yield a full posterior distribution for each parameter — delivering calibrated class probabilities and honest uncertainty even in small samples, rare-event settings, or cases of complete separation where frequentist maximum likelihood estimation collapses.