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| Robuste Variationsinferenz× | Bayes'sche Regression× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 2008-2018 | — |
| Urheber≠ | Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018) | — |
| Typ≠ | Robust approximate Bayesian inference | Bayesian linear model |
| Wegweisende Quelle≠ | Futami, F., Sato, I. & Sugiyama, M. (2018). Variational inference based on robust divergences. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 84:813-822. 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≠ | RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Verwandt≠ | 6 | 2 |
| Zusammenfassung≠ | Robust variational inference (RVI) extends standard variational inference by replacing the Kullback-Leibler divergence with a divergence measure that is less sensitive to outliers and model misspecification — such as the beta-divergence or a Renyi-type divergence. This yields posterior approximations that remain well-behaved even when a fraction of the data departs from the assumed model. | 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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