Comparar métodos

	Modelagem Bayesiana de Equações Estruturais (BSEM)	Regressão por Mínimos Quadrados Ordinários (MQO)
Área	Bayesiano	Econometria
Família	Bayesian methods	Regression model
Ano de origem	2012	2019
Autor original	Bengt Muthén & Tihomir Asparouhov	Wooldridge (textbook treatment); classical least squares
Tipo	Bayesian latent variable model	Linear regression
Fonte seminal	Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Outros nomes	BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Relacionados	6	5
Resumo	Bayesian SEM, introduced by Muthén and Asparouhov in 2012, extends classical structural equation modeling by placing prior distributions on factor loadings, path coefficients, and covariances. Instead of returning a single maximum-likelihood estimate, it uses Markov chain Monte Carlo to produce a full posterior distribution for every parameter, enabling principled uncertainty quantification in models with latent variables.	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).