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| 베이지안 OLS (베이지안 일반 최소 제곱 회귀)× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1971 | 2019 |
| 창시자≠ | Arnold Zellner | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Bayesian linear regression | Linear regression |
| 원전≠ | Zellner, A. (1971). An Introduction to Bayesian Inference in Econometrics. Wiley. ISBN: 978-0471169376 | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 별칭 | Bayesian linear regression, Bayesian normal regression, BLR, Bayesian least squares | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 관련 | 5 | 5 |
| 요약≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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