手法を比較

	ベイズ共分散分析	ベイズ潜在クラス分析（BLCA）
分野	統計学	統計学
系統	Latent structure	Latent structure
提唱年	1995	1990s–2000s
提唱者	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)
種類	Preference measurement / Bayesian hierarchical model	Bayesian latent variable / finite mixture model
原典	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.
別名	Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling	Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model
関連	6	6
概要	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.