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| Bayessche nichtparametrische Methoden× | Bayes'sche Regression× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 1973 (DP); 2006 (GP canonical text) | — |
| Urheber≠ | Ferguson (Dirichlet Process, 1973); Rasmussen & Williams (GP, 2006) | — |
| Typ≠ | Bayesian nonparametric model | Bayesian linear model |
| Wegweisende Quelle≠ | Rasmussen, C.E. & Williams, C.K.I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0262182539 | 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 |
| Aliasnamen≠ | BNP, Dirichlet process mixture, DPM, Gaussian process regression | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Verwandt≠ | 3 | 2 |
| Zusammenfassung≠ | Bayesian nonparametric methods are a family of flexible Bayesian models in which model complexity is not fixed in advance but grows automatically with the data. The two most widely used members are the Dirichlet Process Mixture (DPM), which clusters observations without pre-specifying the number of clusters, and Gaussian Process (GP) regression, which places a prior directly over functions and performs regression or classification without committing to a parametric form. Both frameworks were formalised in the Bayesian nonparametric literature, with the canonical GP treatment given by Rasmussen and Williams (2006). | 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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