Linganisha mbinu

	Uundaji wa Mchanganyiko	Uchambuzi wa Wasifu Ficho (LPA)
Nyanja	Takwimu	Saikometriki
Familia	Latent structure	Latent structure
Mwaka wa asili	1894	2010
Mwanzilishi	Karl Pearson	Lazarsfeld & Henry; Collins & Lanza
Aina	Latent variable / density estimation	Person-centered finite mixture model
Chanzo asilia	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268	Collins, L. M., & Lanza, S. T. (2010). Latent Class and Latent Transition Analysis. Wiley. ISBN: 978-0-470-22839-7
Majina mbadala	finite mixture model, mixture distribution model, FMM, model-based clustering	Continuous Latent Class Analysis, Gaussian Profile Mixture Model, Person-Centered Cluster Analysis, Gizil Profil Analizi
Zinazohusiana	6	2
Muhtasari	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 Profile Analysis (LPA) is a person-centered finite mixture modeling technique that identifies unobserved subgroups — called profiles — within a population based on patterns of scores across multiple continuous indicators. Rooted in Lazarsfeld and Henry's latent structure tradition and formally synthesized for applied behavioral research by Collins and Lanza (2010), LPA assumes that observed heterogeneity in continuous data arises from a discrete number of latent classes, each characterized by a unique multivariate mean profile.