Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Bayesovská konjointní analýza× | Modelování směsí× | |
|---|---|---|
| Obor | Statistika | Statistika |
| Rodina | Latent structure | Latent structure |
| Rok vzniku≠ | 1995 | 1894 |
| Tvůrce≠ | Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964) | Karl Pearson |
| Typ≠ | Preference measurement / Bayesian hierarchical model | Latent variable / density estimation |
| Původní zdroj≠ | Allenby, G. M. & Ginter, J. L. (1995). Using extremes to design products and segment markets. Journal of Marketing Research, 32(4), 392–403. DOI ↗ | McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268 |
| Další názvy | Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling | finite mixture model, mixture distribution model, FMM, model-based clustering |
| Příbuzné | 6 | 6 |
| Shrnutí≠ | 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. | Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components. |
| ScholarGateDatová sada ↗ |
|
|