Comparer des méthodes

	Inférence variationnelle spatiale	Processus Gaussien
Domaine	Bayésien	Apprentissage automatique
Famille	Bayesian methods	Machine learning
Année d'origine	2009	2006 (book); roots in Kriging, 1951)
Auteur d'origine	Titsias (2009) for sparse GP; Rue, Martino & Chopin (2009) for latent Gaussian spatial models	Rasmussen, C. E. & Williams, C. K. I.
Type	Approximate Bayesian inference algorithm	Probabilistic non-parametric model
Source fondatrice	Titsias, M. K. (2009). Variational learning of inducing variables in sparse Gaussian processes. In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 5, pp. 567-574.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	SVI spatial, variational Bayes for spatial data, approximate Bayesian inference for spatial models, variational GP inference	GP, Gaussian Process Regression, GPR, Kriging
Apparentées	5	3
Résumé	Spatial variational inference is a scalable approximate Bayesian method that fits latent Gaussian or Gaussian-process models to georeferenced data by optimising a lower bound on the marginal likelihood. It replaces expensive MCMC sampling with a deterministic optimisation step, making full-posterior uncertainty quantification tractable for large spatial datasets.	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.