Regression modelGIS / spatial
贝叶斯地理加权回归 (BGWR)
贝叶斯地理加权回归 (Bayesian Geographically Weighted Regression, BGWR) 将地理加权回归 (GWR) 的空间变系数框架与贝叶斯推断相结合，对局部变系数施加高斯过程先验。这会为每个位置的每个系数产生完整的后验分布，从而提供原则性的不确定性量化，而不仅仅是点估计。
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来源
- Finley, A. O. (2011). Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods in Ecology and Evolution, 2(2), 143-154. DOI: 10.1111/j.2041-210X.2010.00060.x ↗
- Wheeler, D., & Calder, C. (2007). An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. Journal of Geographical Systems, 9(2), 145-166. DOI: 10.1007/s10109-006-0040-y ↗
如何引用本页
ScholarGate. (2026, June 3). Bayesian Geographically Weighted Regression. ScholarGate. https://scholargate.app/zh/spatial-analysis/bayesian-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.
- 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