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	रोबस्ट वेरिएशन इन्फरेंस (Robust Variational Inference)	बेयसियन रिग्रेशन
क्षेत्र	बायेसियन	बायेसियन
परिवार	Bayesian methods	Bayesian methods
उद्भव वर्ष	2008-2018	—
प्रवर्तक	Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018)	—
प्रकार	Robust approximate Bayesian inference	Bayesian linear model
मौलिक स्रोत	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.	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
उपनाम	RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference	bayesian linear regression, probabilistic regression, bayesian regresyon
संबंधित	6	2
सारांश	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.