Comparer des méthodes

	Régression bayésienne	Modèle de Courbe de Croissance Latente (LGC)
Domaine	Bayésien	Statistique
Famille	Bayesian methods	Latent structure
Année d'origine	—	1990
Auteur d'origine	—	Meredith & Tisak
Type	Bayesian linear model	Latent variable / longitudinal growth model
Source fondatrice	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.
Alias	bayesian linear regression, probabilistic regression, bayesian regresyon	latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli
Apparentées	2	5
Résumé	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.