Сравнение методов

	Байесовская иерархическая модель	Модель латентных кривых роста (LGC)
Область	Байесовские методы	Статистика
Семейство	Bayesian methods	Latent structure
Год появления	2006	1990
Автор метода	Gelman & Hill (2006); Bayesian multilevel tradition	Meredith & Tisak
Тип	hierarchical probabilistic model	Latent variable / longitudinal growth model
Основополагающий источник	Gelman, A. & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.	Meredith, W. & Tisak, J. (1990). Latent Curve Analysis. Psychometrika, 55(1), 107–122.
Другие названия	multilevel Bayes, Bayesian multilevel model, Bayesian HLM, partial pooling model	latent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli
Связанные	4	5
Сводка	Bayesian hierarchical modelling, popularised by Gelman and Hill (2006), is a Bayesian approach to nested data structures — such as students within schools within districts — that estimates separate parameters at each level while allowing those levels to share statistical strength through a mechanism called partial pooling. Where a classical hierarchical linear model treats group means as fixed unknown quantities, the Bayesian version places hyperprior distributions on those group means so that information flows freely across levels, producing more reliable group-level estimates whenever any individual group has few observations.	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.