Linganisha mbinu

	Regressioni Bayesi ya Laini	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)
Nyanja	Mbinu za Bayes	Ekonometriki
Familia	Bayesian methods	Regression model
Mwaka wa asili	2013 (modern reference); foundations 18th–19th century	2019
Mwanzilishi	Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al.	Wooldridge (textbook treatment); classical least squares
Aina	Bayesian linear model	Linear regression
Chanzo asilia	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
Majina mbadala	bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Zinazohusiana	4	5
Muhtasari	Bayesian linear regression is a probabilistic extension of the ordinary linear model, introduced through Bayes' rule and formalised in its modern computational workflow by Gelman et al. (2013). Rather than returning a single point estimate for each coefficient, it combines a user-specified prior distribution with the likelihood of the observed data to produce a full posterior distribution over all parameters, from which credible intervals and posterior predictive distributions are derived.	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).