Methoden vergelijken

	Bayesian Structural Equation Modeling (BSEM)	Gewone Kleinste Kwadraten (GKK) Regressie
Vakgebied	Bayesiaanse statistiek	Econometrie
Familie	Bayesian methods	Regression model
Jaar van ontstaan	2012	2019
Grondlegger	Bengt Muthén & Tihomir Asparouhov	Wooldridge (textbook treatment); classical least squares
Type	Bayesian latent variable model	Linear regression
Oorspronkelijke bron	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
Aliassen	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
Verwant	6	5
Samenvatting	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).