Bayesian methodsBayesian / computational
Spatial Variational Inference
空间变分推断是一种可扩展的近似贝叶斯方法，通过优化边际似然的下界来拟合地理参考数据的潜在高斯或高斯过程模型。它用确定性优化步骤取代昂贵的 MCMC 采样，使得对大型空间数据集进行全后验不确定性量化变得可行。
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来源
- 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 ↗
如何引用本页
ScholarGate. (2026, June 3). Spatial Variational Inference for Latent Gaussian Models. ScholarGate. https://scholargate.app/zh/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.
- 贝叶斯分层模型贝叶斯↔ compare
- 高斯过程机器学习↔ compare
- 空间贝叶斯推断贝叶斯↔ compare
- 空间马尔可夫链蒙特卡洛 (Spatial MCMC)贝叶斯↔ compare
- 变分推断贝叶斯↔ compare