Compara mètodes

	Anàlisi conjunta bayesiana	Anàlisi de Classes Latents (LCA)
Camp	Estadística	Estadística
Família	Latent structure	Latent structure
Any d'origen	1995	1950s–1968
Autor original	Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964)	Paul F. Lazarsfeld
Tipus	Preference measurement / Bayesian hierarchical model	Latent variable / person-centered classification
Font seminal	Allenby, G. M. & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392–403.	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Àlies	Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Relacionats	6	6
Resum	Bayesian conjoint analysis estimates individual-level consumer preference weights for product attributes by combining conjoint choice tasks with a hierarchical Bayesian model. It yields part-worth utilities for each respondent rather than only group averages, enabling precise market simulation and segment discovery even from small per-person choice sets.	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.