Regression modelGIS / spatial
贝叶斯多尺度地理加权回归
贝叶斯多尺度地理加权回归（Bayesian MGWR）通过为每个空间变系数设置贝叶斯先验来扩展 MGWR 框架。每个预测变量都可以拥有自己的带宽——即其自身影响的地理尺度——同时贝叶斯推断用后验采样取代了经典的交替拟合，从而为每个局部系数曲面提供了完整的不确定性量化。
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来源
- Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI: 10.1080/24694452.2017.1352480 ↗
- Li, Z., Fotheringham, A. S., Li, W., & Oshan, T. (2020). Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations. International Journal of Geographical Information Science, 33(1), 155-175. DOI: 10.1080/13658816.2018.1521523 ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Multiscale Geographically Weighted Regression. ScholarGate. https://scholargate.app/zh/spatial-analysis/bayesian-multiscale-geographically-weighted-regression
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.
- 贝叶斯地理加权回归 (BGWR)空间分析↔ compare
- Bayesian Spatial Regression空间分析↔ compare
- 地理加权回归 (GWR)空间分析↔ compare
- 局部空间回归空间分析↔ compare
- 多尺度地理加权回归 (MGWR)空间分析↔ compare
- 空间滞后模型（SAR / 空间自回归）空间分析↔ compare