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| Räumliche Monte-Carlo-Simulation× | Räumliche Bayes'sche Inferenz× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 1970s–1980s | 1991 |
| Urheber≠ | B. D. Ripley and the spatial statistics tradition | Besag, York & Mollie (CAR prior, 1991); Gelfand & colleagues (Bayesian geostatistics, 1990s) |
| Typ≠ | computational simulation | Bayesian hierarchical spatial model |
| Wegweisende Quelle≠ | Ripley, B. D. (1987). Stochastic Simulation. John Wiley & Sons. ISBN: 978-0471818847 | Banerjee, S., Carlin, B. P. & Gelfand, A. E. (2015). Hierarchical Modeling and Analysis for Spatial Data (2nd ed.). CRC Press. ISBN: 978-1439819173 |
| Aliasnamen | spatial MC simulation, Monte Carlo spatial analysis, stochastic spatial simulation, spatial stochastic simulation | Bayesian spatial analysis, Bayesian geostatistics, spatial Bayesian modeling, Bayesian areal modeling |
| Verwandt≠ | 4 | 2 |
| Zusammenfassung≠ | Spatial Monte Carlo simulation applies random sampling methods to spatial problems, generating many stochastic realisations of a spatial process — such as a random field, point pattern, or network — to estimate distributional properties, propagate uncertainty, or test spatial hypotheses. It is a cornerstone technique in geostatistics, spatial epidemiology, ecology, and environmental modelling. | Spatial Bayesian inference applies Bayesian hierarchical modeling to data indexed by geographic location. By placing structured spatial priors on location-specific random effects, the model borrows information from neighboring regions or nearby points, producing smooth, uncertainty-quantified maps of any spatially varying outcome — disease rates, pollution levels, species abundance, or environmental risk. |
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