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| Bayesiansk multidimensionell skalning (BMDS)× | Bayesian Latent Class Analysis (BLCA)× | |
|---|---|---|
| Ämnesområde | Statistik | Statistik |
| Familj | Latent structure | Latent structure |
| Ursprungsår≠ | 2001 | 1990s–2000s |
| Upphovsperson≠ | Oh & Raftery | Lazarsfeld (classical LCA); Bayesian formulation developed through Cheeseman & Stutz (1996) and Dunson & Xing (2009) |
| Typ≠ | Bayesian latent-space dimensionality reduction | Bayesian latent variable / finite mixture model |
| Ursprungskälla≠ | Oh, M.-S. & Raftery, A. E. (2001). Bayesian multidimensional scaling and choice of dimension. Journal of the American Statistical Association, 96(455), 1031–1044. 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 ↗ |
| Alias | Bayesian MDS, BMDS, probabilistic MDS, Bayesian proximity scaling | Bayesian LCA, BLCA, Bayesian mixture of multinomials, Bayesian finite mixture model |
| Närliggande | 6 | 6 |
| Sammanfattning≠ | Bayesian Multidimensional Scaling places objects in a low-dimensional latent space so that inter-object distances reproduce observed dissimilarities, while a full Bayesian treatment quantifies uncertainty in the coordinates, handles missing proximities naturally, and selects the number of dimensions via model comparison rather than heuristic inspection. | 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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