Bayesian methodsBayesian / computational
空间吉布斯采样
空间吉布斯采样将吉布斯采样器——一种逐坐标的马尔可夫链蒙特卡洛算法——应用于观测值在空间上排列且邻近位置在统计上相关的模型。通过利用空间邻域结构所隐含的条件独立性，每个站点在给定其邻居的情况下被逐一更新，从而使马尔可夫随机场、高斯马尔可夫随机场和分层地统计模型的后验推断变得可行。
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来源
- Geman, S. & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 721–741. DOI: 10.1109/TPAMI.1984.4767596 ↗
- Rue, H. & Held, L. (2005). Gaussian Markov Random Fields: Theory and Applications. Chapman & Hall/CRC. ISBN: 978-1584884323
如何引用本页
ScholarGate. (2026, June 3). Spatial Gibbs Sampling for Markov Random Fields and Geostatistical Models. ScholarGate. https://scholargate.app/zh/bayesian/spatial-gibbs-sampling
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
- Gibbs Sampling贝叶斯↔ 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