Linganisha mbinu

	Mchakato Imara wa Gaussian	Usajili wa mstari wa kurudi nyuma kwa uthabiti (Robust Linear Regression)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2011 (formal treatment); GP foundations: Rasmussen & Williams 2006	1964–1987
Mwanzilishi	Jylanki, P.; Vanhatalo, J.; Vehtari, A.	Huber, P. J.; Rousseeuw, P. J.
Aina	Probabilistic non-parametric regression / classification	Outlier-resistant supervised regression
Chanzo asilia	Jylanki, P., Vanhatalo, J., & Vehtari, A. (2011). Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research, 12, 3227–3257.	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.
Majina mbadala	Robust GP, Student-t Process, Heavy-tailed Gaussian Process, Outlier-robust GP	robust regression, M-estimator regression, Huber regression, outlier-resistant regression
Zinazohusiana	5	5
Muhtasari	Robust Gaussian Process (Robust GP) extends the standard Gaussian Process framework by replacing the Gaussian noise likelihood with a heavy-tailed distribution — typically Student-t — so that outliers in the training data exert less influence on the learned function. It retains the full probabilistic, uncertainty-quantifying character of a standard GP while becoming far less sensitive to corrupted or anomalous observations.	Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.