Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beieziešu konžonktu analīze× | Jaukto sadalījumu modelēšana× | |
|---|---|---|
| Nozare | Statistika | Statistika |
| Saime | Latent structure | Latent structure |
| Izcelsmes gads≠ | 1995 | 1894 |
| Autors≠ | Allenby & Ginter (hierarchical Bayes formulation); conjoint roots in Luce & Tukey (1964) | Karl Pearson |
| Tips≠ | Preference measurement / Bayesian hierarchical model | Latent variable / density estimation |
| Pirmavots≠ | 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 |
| Citi nosaukumi | Bayesian CA, hierarchical Bayes conjoint, HB conjoint, Bayesian preference modeling | finite mixture model, mixture distribution model, FMM, model-based clustering |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | 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. |
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