Linganisha mbinu

	Uchanganuzi wa Regresheni Uzito wa Kijiografia wa Mizani Mingi wa Kibayesia	Urekebishaji wa Kijiografia wa Bayesian (BGWR)
Nyanja	Uchanganuzi wa Kimaeneo	Uchanganuzi wa Kimaeneo
Familia	Regression model	Regression model
Mwaka wa asili	2017-2020	2007
Mwanzilishi	Fotheringham, Yang & Kang (MGWR); Bayesian extension by Li and co-authors	Wheeler & Calder (2007); Finley (2011)
Aina	Spatially varying coefficient regression	Bayesian spatially varying coefficient regression
Chanzo asilia	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.	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.
Majina mbadala	Bayesian MGWR, B-MGWR, Bayesian multiscale GWR, Bayesian spatially varying coefficient model	BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression
Zinazohusiana	6	5
Muhtasari	Bayesian Multiscale Geographically Weighted Regression (Bayesian MGWR) extends the MGWR framework by placing Bayesian priors on each spatially varying coefficient. Each predictor is allowed its own bandwidth — its own geographic scale of influence — while Bayesian inference replaces classical back-fitting with posterior sampling, yielding full uncertainty quantification for every local coefficient surface.	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.