Comparer des méthodes

	Processus Gaussien Bayésien	Processus Gaussien
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1978–2006	2006 (book); roots in Kriging, 1951)
Auteur d'origine	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.	Rasmussen, C. E. & Williams, C. K. I.
Type	Probabilistic kernel model	Probabilistic non-parametric 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	GP regression, GPR, Gaussian process model, GP classifier	GP, Gaussian Process Regression, GPR, Kriging
Apparentées	3	3
Résumé	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.	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.