Comparer des méthodes

	Analyse conjointe bayésienne	Modélisation par mélange
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1995	1894
Auteur d'origine	Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964)	Karl Pearson
Type	Preference measurement / Bayesian hierarchical model	Latent variable / density estimation
Source fondatrice	Allenby, G. M. & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392–403.	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Alias	Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling	finite mixture model, mixture distribution model, FMM, model-based clustering
Apparentées	6	6
Résumé	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.	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.