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| Phân tích kết hợp Bayes× | Phân tích Lớp Ẩn Bayes (BLCA)× | |
|---|---|---|
| Lĩnh vực | Thống kê | Thống kê |
| Họ | Latent structure | Latent structure |
| Năm ra đời≠ | 1995 | 1990s–2000s |
| Người khởi xướng≠ | 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) |
| Loại≠ | Preference measurement / Bayesian hierarchical model | Bayesian latent variable / finite mixture model |
| Công trình gốc≠ | Allenby, G. M. & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392–403. DOI ↗ | Dunson, D. B. & Xing, C. (2009). Nonparametric Bayes modeling of multivariate categorical data. Journal of the American Statistical Association, 104(487), 1042–1051. DOI ↗ |
| Tên gọi khác | Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling | Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model |
| Liên quan | 6 | 6 |
| Tóm tắt≠ | 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. |
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