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	Bayesowskie modelowanie równań strukturalnych (BSEM)	Regresja bayesowska	Model krzywej wzrostu utajonego (LGC)
Dziedzina	Statystyka bayesowska	Statystyka bayesowska	Statystyka
Rodzina	Bayesian methods	Bayesian methods	Latent structure
Rok powstania	2012	—	1990
Twórca	Bengt Muthén & Tihomir Asparouhov	—	Meredith & Tisak
Typ	Bayesian latent variable model	Bayesian linear model	Latent variable / longitudinal growth model
Źródło pierwotne	Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335.	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	Meredith, W. & Tisak, J. (1990). Latent Curve Analysis. Psychometrika, 55(1), 107–122.
Inne nazwy	BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modeli	bayesian linear regression, probabilistic regression, bayesian regresyon	latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli
Pokrewne	6	2	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.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.	The latent growth curve model is a structural equation modelling approach introduced by Meredith and Tisak (1990) for analysing change over time. It treats each individual's starting point (intercept) and rate of change (slope) as latent variables, simultaneously estimating the average trajectory across the sample and the extent to which individuals differ in their own trajectories.