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	Analisi Canonica delle Correlazioni Bayesiana (Bayesian CCA)	Analisi Fattoriale Esplorativa Bayesiana (BEFA)
Campo	Statistica	Psicometria
Famiglia	Latent structure	Latent structure
Anno di origine	2005-2013	2004 (Bayesian formulation); factor analysis roots: 1904
Ideatore	Francis Bach & Michael Jordan (probabilistic formulation, 2005); Klami, Virtanen & Kaski (fully Bayesian treatment, 2013)	Lopes & West (seminal Bayesian treatment); roots in classical factor analysis (Spearman, 1904)
Tipo	Latent variable model / dimensionality reduction	Probabilistic latent variable model
Fonte seminale	Bach, F. R. & Jordan, M. I. (2005). A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley.	Lopes, H. F. & West, M. (2004). Bayesian model assessment in factor analysis. Statistica Sinica, 14(1), 41–67.
Alias	Bayesian CCA, probabilistic CCA, BCCA	Bayesian factor analysis, BEFA, Bayesian common factor model, probabilistic factor analysis
Correlati	5	4
Sintesi	Bayesian canonical correlation analysis is a probabilistic generative model that identifies shared latent structure between two or more sets of observed variables. It extends classical CCA by placing priors on model parameters, enabling principled uncertainty quantification, automatic determination of the number of shared dimensions, and robustness when sample sizes are small relative to dimensionality.	Bayesian exploratory factor analysis applies a full probabilistic framework to the common factor model. By placing prior distributions over factor loadings and unique variances, it yields posterior distributions rather than point estimates, quantifies uncertainty around every loading, and can treat the number of factors as an unknown to be inferred from data.