Salīdzināt metodes

	Latent Class Analysis (LCA)	Jaukto sadalījumu modelēšana
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1950s–1968	1894
Autors	Paul F. Lazarsfeld	Karl Pearson
Tips	Latent variable / person-centered classification	Latent variable / density estimation
Pirmavots	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Citi nosaukumi	LCA, latent class model, latent categorical analysis, finite mixture of multinomials	finite mixture model, mixture distribution model, FMM, model-based clustering
Saistītās	6	6
Kopsavilkums	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.	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.