Methoden vergelijken

	Bayesian Regressie	Latent Growth Curve Model (LGC)
Vakgebied	Bayesiaanse statistiek	Statistiek
Familie	Bayesian methods	Latent structure
Jaar van ontstaan	—	1990
Grondlegger	—	Meredith & Tisak
Type	Bayesian linear model	Latent variable / longitudinal growth model
Oorspronkelijke bron	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.
Aliassen	bayesian linear regression, probabilistic regression, bayesian regresyon	latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli
Verwant	2	5
Samenvatting	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.