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	混合モデル (Mixture Modeling)	因子分析（EFA）
分野	統計学	統計学
系統	Latent structure	Latent structure
提唱年	1894	—
提唱者	Karl Pearson	—
種類	Latent variable / density estimation	Latent variable / dimension reduction
原典	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.
別名	finite mixture model, mixture distribution model, FMM, model-based clustering	common factor analysis, açımlayıcı faktör analizi, factor analysis
関連	6	4
概要	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.