Comparar métodos

	Regressão Linear Múltipla Bayesiana	Regressão por Mínimos Quadrados Ordinários (MQO)
Área	Estatística	Econometria
Família	Regression model	Regression model
Ano de origem	1971	2019
Autor original	Arnold Zellner (econometric formulation); broader development by Harold Jeffreys and Gelman et al.	Wooldridge (textbook treatment); classical least squares
Tipo	Bayesian parametric regression	Linear regression
Fonte seminal	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
Outros nomes	Bayesian MLR, Bayesian linear regression, Bayesian multivariate regression, conjugate normal-inverse-gamma regression	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionados	6	5
Resumo	Bayesian Multiple Linear Regression models a continuous outcome as a linear combination of several predictors, but instead of producing a single point estimate it yields a full posterior distribution over all regression coefficients and the error variance. This makes uncertainty quantification explicit and allows seamlessly incorporating prior knowledge from theory or previous studies.	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).