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	Bayesowskie modelowanie równań strukturalnych (BSEM)	Regresja metodą najmniejszych kwadratów (OLS)
Dziedzina	Statystyka bayesowska	Ekonometria
Rodzina	Bayesian methods	Regression model
Rok powstania	2012	2019
Twórca	Bengt Muthén & Tihomir Asparouhov	Wooldridge (textbook treatment); classical least squares
Typ	Bayesian latent variable model	Linear regression
Źródło pierwotne	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
Inne nazwy	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
Pokrewne	6	5
Podsumowanie	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).