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| Térbeli variációs következtetés× | Variational Inference× | |
|---|---|---|
| Tudományterület | Bayes-statisztika | Bayes-statisztika |
| Módszercsalád | Bayesian methods | Bayesian methods |
| Keletkezés éve≠ | 2009 | 1999 |
| Megalkotó≠ | Titsias (2009) for sparse GP; Rue, Martino & Chopin (2009) for latent Gaussian spatial models | Jordan, Ghahramani, Jaakkola & Saul |
| Típus≠ | Approximate Bayesian inference algorithm | Approximate Bayesian inference |
| Alapmű≠ | 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 ↗ | Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233. DOI ↗ |
| Alternatív nevek≠ | SVI spatial, variational Bayes for spatial data, approximate Bayesian inference for spatial models, variational GP inference | VI, variational Bayes, VB, mean-field variational inference |
| Kapcsolódó≠ | 5 | 4 |
| Összefoglaló≠ | 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. | Variational inference (VI) is a family of techniques that turn Bayesian posterior computation into an optimisation problem. Instead of drawing samples from the exact posterior — as Markov chain Monte Carlo does — VI posits a simpler, tractable family of distributions and finds the member of that family closest to the true posterior by maximising the evidence lower bound (ELBO). Introduced in its modern graphical-model form by Jordan, Ghahramani, Jaakkola and Saul (1999) and given a comprehensive statistical treatment by Blei, Kucukelbir and McAuliffe (2017), VI is now the standard scalable inference engine in probabilistic machine learning. |
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