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Bayesian Geographically Weighted Regression
Bayesian Geographically Weighted Regression combines the spatially varying coefficient framework of GWR with Bayesian inference, placing Gaussian process priors on the locally varying regression coefficients. This yields full posterior distributions over each coefficient at every location, providing principled uncertainty quantification rather than only point estimates.
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Bayesian Geographically Weighted Regression
Taksonomski zapis metode · regression-model / spatial-analysis
- 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
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