Salīdzināt metodes

	Beieziešu konžonktu analīze	Beieziešu latentās klases analīze (BLCA)
Nozare	Statistika	Statistika
Saime	Latent structure	Latent structure
Izcelsmes gads	1995	1990s–2000s
Autors	Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964)	Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009)
Tips	Preference measurement / Bayesian hierarchical model	Bayesian latent variable / finite mixture model
Pirmavots	Allenby, G. M. & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392–403.	Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051.
Citi nosaukumi	Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
Saistītās	6	6
Kopsavilkums	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.	Bayesian latent class analysis extends classical LCA by placing prior distributions on all model parameters and using posterior inference — typically via MCMC — to classify individuals into unobserved categorical groups, quantify uncertainty around class membership, and select the number of classes in a principled, probabilistic way.