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| Bayes-féle konzekvens analízis× | Látens Osztály Elemzés (LCA)× | |
|---|---|---|
| Tudományterület | Statisztika | Statisztika |
| Módszercsalád | Latent structure | Latent structure |
| Keletkezés éve≠ | 1995 | 1950s–1968 |
| Megalkotó≠ | Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964) | Paul F. Lazarsfeld |
| Típus≠ | Preference measurement / Bayesian hierarchical model | Latent variable / person-centered classification |
| Alapmű≠ | Allenby, G. M. & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392–403. DOI ↗ | Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231. DOI ↗ |
| Alternatív nevek | Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling | LCA, latent class model, latent categorical analysis, finite mixture of multinomials |
| Kapcsolódó | 6 | 6 |
| Összefoglaló≠ | 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. | Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data. |
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