Comparer des méthodes

	Processus Gaussien	Optimisation bayésienne
Domaine	Apprentissage automatique	Optimisation
Famille	Machine learning	Process / pipeline
Année d'origine	2006 (book); roots in Kriging, 1951)	1975 (foundational); 2012 (ML standard)
Auteur d'origine	Rasmussen, C. E. & Williams, C. K. I.	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)
Type	Probabilistic non-parametric model	Sequential model-based black-box optimization
Source fondatrice	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.
Alias	GP, Gaussian Process Regression, GPR, Kriging	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO
Apparentées	3	2
Résumé	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.	Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones.