Comparer des méthodes

	Processus Gaussien Robuste	Processus Gaussien Bayésien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2011 (formal treatment); GP foundations: Rasmussen & Williams 2006	1978–2006
Auteur d'origine	Jylanki, P.; Vanhatalo, J.; Vehtari, A.	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic non-parametric regression / classification	Probabilistic kernel model
Source fondatrice	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
Alias	Robust GP, Student-t Process, Heavy-tailed Gaussian Process, Outlier-robust GP	GP regression, GPR, Gaussian process model, GP classifier
Apparentées	5	3
Résumé	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 Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning.