方法对比
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| 贝叶斯联合分析× | 贝叶斯混合模型× | |
|---|---|---|
| 领域 | 统计学 | 统计学 |
| 方法族 | Latent structure | Latent structure |
| 起源年份≠ | 1995 | 1997 (Richardson & Green Bayesian formulation) |
| 提出者≠ | Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964) | Richardson & Green (seminal Bayesian treatment, 1997); broader Bayesian mixture roots trace to Dempster, Laird & Rubin (EM, 1977) and Titterington, Smith & Makov (1985) |
| 类型≠ | Preference measurement / Bayesian hierarchical model | Latent-class / model-based clustering |
| 开创性文献≠ | Allenby, G. M. & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392–403. DOI ↗ | Fruhwirth-Schnatter, S., Celeux, G. & Robert, C. P. (Eds.) (2019). Handbook of Mixture Analysis. CRC Press / Chapman & Hall. ISBN: 9780367733995 |
| 别名 | Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling | Bayesian mixture model, BMM, Bayesian model-based clustering, Bayesian finite mixture |
| 相关≠ | 6 | 4 |
| 摘要≠ | 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 mixture modeling represents the population as a weighted sum of K component distributions and estimates all unknowns — mixing weights, component parameters, and even the number of components — through posterior inference. It extends classical mixture analysis by placing priors on every parameter and quantifying uncertainty over latent group assignments rather than treating them as fixed. |
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