Comparer des méthodes

	Processus Gaussien Régularisé	Processus Gaussien Bayésien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006 (canonical formulation); kernel regularization roots 1990s	1978–2006
Auteur d'origine	Rasmussen, C. E. & Williams, C. K. I.	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic kernel model with regularization	Probabilistic kernel model
Source fondatrice	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	Regularized GP, GP with noise regularization, sparse regularized Gaussian process, regularized Gaussian process regression	GP regression, GPR, Gaussian process model, GP classifier
Apparentées	4	3
Résumé	A Regularized Gaussian Process (GP) is a probabilistic kernel-based model that places a prior over functions and explicitly controls overfitting through a noise regularization parameter — the observation noise variance — that prevents the model from memorizing training labels. It produces calibrated uncertainty estimates alongside predictions, making it uniquely suited to small or expensive datasets where knowing how confident the model is matters as much as the prediction itself.	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.