Salīdzināt metodes

	Jaukto sadalījumu modelēšana	Eksploratīvā faktoru analīze (EFA)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1894	—
Autors	Karl Pearson	—
Tips	Latent variable / density estimation	Latent variable / dimension reduction
Pirmavots	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268	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.
Citi nosaukumi	finite mixture model, mixture distribution model, FMM, model-based clustering	common factor analysis, açımlayıcı faktör analizi, factor analysis
Saistītās	6	4
Kopsavilkums	Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.	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.