Compara mètodes

	Process Gaussian Robusta	Regressió Lineal Robusta
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2011 (formal treatment); GP foundations: Rasmussen & Williams 2006	1964–1987
Autor original	Jylanki, P.; Vanhatalo, J.; Vehtari, A.	Huber, P. J.; Rousseeuw, P. J.
Tipus	Probabilistic non-parametric regression / classification	Outlier-resistant supervised regression
Font seminal	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.
Àlies	Robust GP, Student-t Process, Heavy-tailed Gaussian Process, Outlier-robust GP	robust regression, M-estimator regression, Huber regression, outlier-resistant regression
Relacionats	5	5
Resum	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.