Сравнение на методи

	Байесов модел на проста линейна регресия	Метод на най-малките квадрати (МНК)
Област	Статистика	Иконометрия
Семейство	Regression model	Regression model
Година на възникване	Early 19th century; textbook synthesis 2013	2019
Създател	Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al.	Wooldridge (textbook treatment); classical least squares
Тип	Bayesian linear regression	Linear regression
Основополагащ източник	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	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Други названия	Bayesian SLR, Bayesian univariate regression, probabilistic simple linear regression, Bayesian linear model	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Свързани	6	5
Резюме	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.	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).