Compară metode

	Analiză Conjoint Bayesiană	Analiza claselor latente (LCA)
Domeniu	Statistică	Statistică
Familie	Latent structure	Latent structure
Anul apariției	1995	1950s–1968
Autorul original	Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964)	Paul F. Lazarsfeld
Tip	Preference measurement / Bayesian hierarchical model	Latent variable / person-centered classification
Sursa seminală	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.
Denumiri alternative	Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Înrudite	6	6
Rezumat	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.