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Robust variasjonsinferens×Bayesiansk regresjon×
FagfeltBayesianskBayesiansk
FamilieBayesian methodsBayesian methods
Opprinnelsesår2008-2018
OpphavspersonFujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018)
TypeRobust approximate Bayesian inferenceBayesian linear model
Opprinnelig kildeFutami, 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
AliasRVI, robust VI, outlier-robust variational Bayes, power-divergence variational inferencebayesian linear regression, probabilistic regression, bayesian regresyon
Relaterte62
SammendragRobust 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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ScholarGateSammenlign metoder: Robust Variational Inference · Bayesian Regression. Hentet 2026-06-15 fra https://scholargate.app/no/compare