방법 비교

	베이지안 분위수 회귀	베이즈 일반화 선형 모형
분야	통계학	통계학
계열	Regression model	Regression model
기원 연도	2001–2011	1989 (GLM); 1995 (Bayesian BDA)
창시자	Kozumi & Kobayashi; building on Yu & Moyeed (2001)	McCullagh & Nelder (GLM framework); Bayesian treatment formalized by Gelman et al.
유형	Bayesian semiparametric regression	Bayesian regression model
원전	Kozumi, H., & Kobayashi, G. (2011). Gibbs sampling methods for Bayesian quantile regression. Journal of Statistical Computation and Simulation, 81(11), 1565–1578.	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	BQR, Bayesian quantile regression model, asymmetric Laplace Bayesian regression, posterior quantile regression	Bayesian GLM, Bayesian GLIM, Bayesian generalized linear regression, Bayes GLM
관련	6	6
요약	Bayesian Quantile Regression estimates the full posterior distribution of regression coefficients at any chosen quantile of the outcome. By combining the asymmetric Laplace likelihood with prior distributions over the coefficients, it delivers uncertainty-quantified estimates of conditional quantiles — such as the median, the 10th, or the 90th percentile — without assuming Gaussian errors.	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.