Usporedite metode

	Bayesovska regresija	Model latentnog rasta krivulje (LGC)
Područje	Bayesovska statistika	Statistika
Obitelj	Bayesian methods	Latent structure
Godina nastanka	—	1990
Tvorac	—	Meredith & Tisak
Vrsta	Bayesian linear model	Latent variable / longitudinal growth model
Temeljni izvor	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.
Drugi nazivi	bayesian linear regression, probabilistic regression, bayesian regresyon	latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli
Srodne	2	5
Sažetak	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.