Comparar métodos

	Modelagem de Misturas	Análise de Classes Latentes (LCA)
Área	Estatística	Estatística
Família	Latent structure	Latent structure
Ano de origem	1894	1950s–1968
Autor original	Karl Pearson	Paul F. Lazarsfeld
Tipo	Latent variable / density estimation	Latent variable / person-centered classification
Fonte seminal	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Outros nomes	finite mixture model, mixture distribution model, FMM, model-based clustering	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Relacionados	6	6
Resumo	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.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.