方法对比

	贝叶斯因子分析	Bayesian Regression
领域	贝叶斯	贝叶斯
方法族	Bayesian methods	Bayesian methods
起源年份	2004	—
提出者	Lopes & West (2004) for Bayesian model assessment in factor analysis	—
类型	Bayesian latent variable model	Bayesian linear model
开创性文献	Lopes, H. F. & West, M. (2004). Bayesian Model Assessment in Factor Analysis. Statistica Sinica, 14(1), 41–67.	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
别名	Bayesian EFA, Bayesian CFA, Bayesçi Faktör Analizi, probabilistic factor analysis	bayesian linear regression, probabilistic regression, bayesian regresyon
相关	7	2
摘要	Bayesian Factor Analysis is a probabilistic latent-variable method that places prior distributions on the factor loading matrix and the residual variances, then infers a full posterior over these parameters from the observed data. Developed prominently in the Bayesian framework by Lopes and West (2004), it extends classical exploratory and confirmatory factor analysis by quantifying uncertainty in every estimated loading rather than reporting single point estimates.	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.