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| Robuste Bayes'sche Modell-Mittelung× | Bayes'sche Regression× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 1999–2012 | — |
| Urheber≠ | Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others | — |
| Typ≠ | Bayesian model selection and averaging | Bayesian linear model |
| Wegweisende Quelle≠ | Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401. link ↗ | 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≠ | robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Verwandt≠ | 6 | 2 |
| Zusammenfassung≠ | Robust Bayesian model averaging extends standard BMA by replacing sensitive conjugate priors with heavy-tailed or mixture priors (e.g., mixtures of g-priors), and optionally robust likelihoods, so that posterior model probabilities and averaged estimates remain stable when data contain outliers, influential observations, or when the prior on model parameters would otherwise dominate the results. | 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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