Rumlig variationel inferens
Rumlig variationel inferens er en skalerbar, approksimativ Bayesiansk metode, der tilpasser latente Gaussiske eller Gaussisk-procesmodeller til georefererede data ved at optimere en nedre grænse for den marginale likelihood. Den erstatter dyr MCMC-sampling med et deterministisk optimeringstrin, hvilket gør fuld-posterior usikkerhedskvantificering håndterbar for store rumlige datasæt.
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Method map
The neighbourhood of related methods — select a node to explore.
Kilder
- 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 ↗
- Rue, H., Martino, S., & Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B, 71(2), 319-392. DOI: 10.1111/j.1467-9868.2008.00700.x ↗
Sådan citerer du denne side
ScholarGate. (2026, June 3). Spatial Variational Inference for Latent Gaussian Models. ScholarGate. https://scholargate.app/da/bayesian/spatial-variational-inference
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Bayesiansk hierarkisk modelBayesiansk↔ compare
- Gaussisk procesMaskinlæring↔ compare
- Rumslig Bayesiansk InferensBayesiansk↔ compare
- Spatial MCMCBayesiansk↔ compare
- VariationsinferensBayesiansk↔ compare
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