Compara mètodes

	Inferencia Variacional Espacial	Processos Gaussianos
Camp	Bayesià	Aprenentatge automàtic
Família	Bayesian methods	Machine learning
Any d'origen	2009	2006 (book); roots in Kriging, 1951)
Autor original	Titsias (2009) for sparse GP; Rue, Martino & Chopin (2009) for latent Gaussian spatial models	Rasmussen, C. E. & Williams, C. K. I.
Tipus	Approximate Bayesian inference algorithm	Probabilistic non-parametric model
Font seminal	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
Àlies	SVI spatial, variational Bayes for spatial data, approximate Bayesian inference for spatial models, variational GP inference	GP, Gaussian Process Regression, GPR, Kriging
Relacionats	5	3
Resum	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.