So sánh phương pháp

	Hồi quy Tuyến tính Đơn giản Bayes	Hồi quy tuyến tính đơn giản
Lĩnh vực	Thống kê	Thống kê
Họ	Regression model	Regression model
Năm ra đời	Early 19th century; textbook synthesis 2013	1805
Người khởi xướng	Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al.	Adrien-Marie Legendre (least squares, 1805); Francis Galton (regression concept, 1886)
Loại	Bayesian linear regression	Parametric bivariate regression
Công trình gốc	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	Legendre, A. M. (1805). Nouvelles méthodes pour la détermination des orbites des comètes. Firmin Didot, Paris. [Appendix: Sur la méthode des moindres quarrés, pp. 72–80]
Tên gọi khác	Bayesian SLR, Bayesian univariate regression, probabilistic simple linear regression, Bayesian linear model	SLR, ordinary least squares regression, OLS regression, bivariate regression
Liên quan	6	7
Tóm tắt	Bayesian Simple Linear Regression models the relationship between a continuous outcome and a single predictor by combining a Gaussian likelihood with prior distributions over the intercept, slope, and error variance. The result is a full posterior distribution over all parameters, providing probabilistic uncertainty quantification rather than a single point estimate.	Simple linear regression is the foundational parametric method for modelling a straight-line relationship between one continuous predictor and one continuous outcome, estimating the slope and intercept by ordinary least squares (OLS). The least squares principle was first published by Adrien-Marie Legendre in 1805, and Francis Galton introduced the concept of regression to the mean in 1886, coining the term that names the entire family of methods.