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	ベイズ回帰	因子分析（EFA）
分野	ベイズ	統計学
系統	Bayesian methods	Latent structure
提唱年	—	—
提唱者	—	—
種類	Bayesian linear model	Latent variable / dimension reduction
原典	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	Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.
別名	bayesian linear regression, probabilistic regression, bayesian regresyon	common factor analysis, açımlayıcı faktör analizi, factor analysis
関連	2	4
概要	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.	Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.