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ベイズ構造方程式モデリング(BSEM)×ベイズ回帰×潜在成長曲線モデル (LGC)×
分野ベイズベイズ統計学
系統Bayesian methodsBayesian methodsLatent structure
提唱年20121990
提唱者Bengt Muthén & Tihomir AsparouhovMeredith & Tisak
種類Bayesian latent variable modelBayesian linear modelLatent variable / longitudinal growth model
原典Muthén, B. & Asparouhov, T. (2012). Bayesian SEM: A More Flexible Representation of Substantive Theory. Psychological Methods, 17(3), 313–335. link ↗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: 978-1439840955Meredith, W. & Tisak, J. (1990). Latent Curve Analysis. Psychometrika, 55(1), 107–122. DOI ↗
別名BSEM, Bayesian latent variable model, approximate zero constraints SEM, Bayesçi Yapısal Eşitlik Modelibayesian linear regression, probabilistic regression, bayesian regresyonlatent growth model, LGC, growth curve model, Gizil Büyüme Eğrisi Modeli
関連625
概要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.
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ScholarGate手法を比較: Bayesian SEM · Bayesian Regression · LGC Model. 2026-06-19に以下より取得 https://scholargate.app/ja/compare