Methoden vergleichen

	Robuster Gauß-Prozess	Gauß-Prozess
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2011 (formal treatment); GP foundations: Rasmussen & Williams 2006	2006 (book); roots in Kriging, 1951)
Urheber	Jylanki, P.; Vanhatalo, J.; Vehtari, A.	Rasmussen, C. E. & Williams, C. K. I.
Typ	Probabilistic non-parametric regression / classification	Probabilistic non-parametric model
Wegweisende Quelle	Jylanki, P., Vanhatalo, J., & Vehtari, A. (2011). Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research, 12, 3227–3257.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Aliasnamen	Robust GP, Student-t Process, Heavy-tailed Gaussian Process, Outlier-robust GP	GP, Gaussian Process Regression, GPR, Kriging
Verwandt	5	3
Zusammenfassung	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.	A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.