Salīdzināt metodes

	Jaukto sadalījumu modelēšana	Latent Class Analysis (LCA)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1894	1950s–1968
Autors	Karl Pearson	Paul F. Lazarsfeld
Tips	Latent variable / density estimation	Latent variable / person-centered classification
Pirmavots	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.
Citi nosaukumi	finite mixture model, mixture distribution model, FMM, model-based clustering	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Saistītās	6	6
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.	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.