方法对比

	贝叶斯加权最小二乘法 (Bayesian WLS)	贝叶斯普通最小二乘回归 (Bayesian OLS)
领域	计量经济学	计量经济学
方法族	Regression model	Regression model
起源年份	1971	1971
提出者	Arnold Zellner (Bayesian econometrics framework)	Arnold Zellner
类型	Bayesian weighted regression	Bayesian linear regression
开创性文献	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley, New York. ISBN: 978-0471169376	Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376
别名	Bayesian weighted regression, BWLS, Bayesian heteroscedastic regression, weighted Bayesian linear regression	Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares
相关	4	5
摘要	Bayesian Weighted Least Squares combines the classical WLS weighting scheme — which downweights observations with high error variance — with Bayesian prior distributions over the regression coefficients and error variance. The result is a posterior distribution that reflects both the data likelihood and prior beliefs, providing full uncertainty quantification in heteroscedastic settings.	Bayesian OLS combines the classical linear regression likelihood with prior distributions over the coefficients and error variance. Rather than reporting point estimates, it produces full posterior distributions that quantify both estimated effects and their uncertainty. The approach is especially valuable when prior knowledge is available or when samples are small.