Salīdzināt metodes

	Beiziešu diskriminējošā analīze	Bayesiskā šķirības faktoru analīze (BCFA)
Nozare	Statistika	Psihometrija
Saime	Latent structure	Latent structure
Izcelsmes gads	1964	2007–2012
Autors	Seymour Geisser	Sik-Yum Lee; Bengt Muthén and Tihomir Asparouhov
Tips	Supervised classification / Bayesian inference	Bayesian latent variable model
Pirmavots	Geisser, S. (1964). Posterior odds for multivariate normal classifications. Journal of the Royal Statistical Society, Series B, 26(1), 69–76.	Lee, S.-Y. (2007). Structural Equation Modeling: A Bayesian Approach. Wiley. ISBN: 978-0470024232
Citi nosaukumi	BDA, Bayesian linear discriminant analysis, Bayesian quadratic discriminant analysis, Bayesian classification	BCFA, Bayesian CFA, Bayesian structural equation measurement model, Bayes-CFA
Saistītās	4	4
Kopsavilkums	Bayesian discriminant analysis assigns observations to predefined groups by combining a multivariate Gaussian likelihood for each class with prior distributions over the class means and covariance matrices. Posterior predictive probabilities replace point-estimate decision boundaries, providing principled uncertainty quantification for classification in small or high-dimensional samples.	Bayesian confirmatory factor analysis tests a pre-specified factor structure using Bayesian inference. Instead of point estimates with p-values, it produces full posterior distributions for loadings, factor correlations, and residual variances, allowing the researcher to incorporate prior knowledge and propagate parameter uncertainty naturally.