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| Räumliche Variationsinferenz× | Gauß-Prozess× | |
|---|---|---|
| Fachgebiet≠ | Bayes-Statistik | Maschinelles Lernen |
| Familie≠ | Bayesian methods | Machine learning |
| Entstehungsjahr≠ | 2009 | 2006 (book); roots in Kriging, 1951) |
| Urheber≠ | Titsias (2009) for sparse GP; Rue, Martino & Chopin (2009) for latent Gaussian spatial models | Rasmussen, C. E. & Williams, C. K. I. |
| Typ≠ | Approximate Bayesian inference algorithm | Probabilistic non-parametric model |
| Wegweisende Quelle≠ | 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. link ↗ | Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9 |
| Aliasnamen | SVI spatial, variational Bayes for spatial data, approximate Bayesian inference for spatial models, variational GP inference | GP, Gaussian Process Regression, GPR, Kriging |
| Verwandt≠ | 5 | 3 |
| Zusammenfassung≠ | 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. |
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