Confronta i metodi

	Gaussian Process Robusto	Regressione Lineare Robusta
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	2011 (formal treatment); GP foundations: Rasmussen & Williams 2006	1964–1987
Ideatore	Jylanki, P.; Vanhatalo, J.; Vehtari, A.	Huber, P. J.; Rousseeuw, P. J.
Tipo	Probabilistic non-parametric regression / classification	Outlier-resistant supervised regression
Fonte seminale	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.
Alias	Robust GP, Student-t Process, Heavy-tailed Gaussian Process, Outlier-robust GP	robust regression, M-estimator regression, Huber regression, outlier-resistant regression
Correlati	5	5
Sintesi	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.