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| Modèle de Mélange à Processus de Dirichlet× | Régression bayésienne× | |
|---|---|---|
| Domaine | Bayésien | Bayésien |
| Famille | Bayesian methods | Bayesian methods |
| Année d'origine≠ | 1973 | — |
| Auteur d'origine≠ | Ferguson (1973); mixture model formulation by Lo (1984) | — |
| Type≠ | Nonparametric Bayesian mixture model | Bayesian linear model |
| Source fondatrice≠ | Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2), 209–230. DOI ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Alias≠ | DPMM, DP mixture model, infinite mixture model, Dirichlet process mixture | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Apparentées≠ | 3 | 2 |
| Résumé≠ | The Dirichlet Process Mixture Model (DPMM) is a nonparametric Bayesian clustering method introduced through Ferguson's (1973) Dirichlet process prior that places a probability distribution over distributions. Unlike finite mixture models, the DPMM does not require the analyst to specify the number of clusters in advance; instead it infers the number of components from the data, allowing an effectively unbounded mixture that grows as more observations arrive. | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. |
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