Spatial Gibbs Sampling
Spatial Gibbs sampling applies the Gibbs sampler — a coordinate-wise Markov chain Monte Carlo algorithm — to models where observations are arranged in space and nearby locations are statistically dependent. By exploiting the conditional independence implied by a spatial neighbourhood structure, each site is updated one at a time given its neighbours, making posterior inference tractable for Markov random fields, Gaussian random fields, and hierarchical geostatistical models.
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Method map
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Allikad
- 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
Kuidas sellele lehele viidata
ScholarGate. (2026, June 3). Spatial Gibbs Sampling for Markov Random Fields and Geostatistical Models. ScholarGate. https://scholargate.app/et/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.
- Bayesian Hierarchical ModelBayesi meetodid↔ compare
- Gibbs SamplingBayesi meetodid↔ compare
- Ruumiline Bayesi järeldamineBayesi meetodid↔ compare
- Ruumi MCMCBayesi meetodid↔ compare
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