方法对比

	Spatial Variational Inference	高斯过程
领域	贝叶斯	机器学习
方法族	Bayesian methods	Machine learning
起源年份	2009	2006 (book); roots in Kriging, 1951)
提出者	Titsias (2009) for sparse GP; Rue, Martino & Chopin (2009) for latent Gaussian spatial models	Rasmussen, C. E. & Williams, C. K. I.
类型	Approximate Bayesian inference algorithm	Probabilistic non-parametric model
开创性文献	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
别名	SVI spatial, variational Bayes for spatial data, approximate Bayesian inference for spatial models, variational GP inference	GP, Gaussian Process Regression, GPR, Kriging
相关	5	3
摘要	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.