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	बेयसियन मल्टीस्केल ज्योग्राफिकली वेटेड रिग्रेशन	स्थानीय स्थानिक प्रतिगमन (Local Spatial Regression)
क्षेत्र	स्थानिक विश्लेषण	स्थानिक विश्लेषण
परिवार	Regression model	Regression model
उद्भव वर्ष	2017-2020	1996
प्रवर्तक	Fotheringham, Yang & Kang (MGWR); Bayesian extension by Li and co-authors	Brunsdon, Fotheringham & Charlton
प्रकार	Spatially varying coefficient regression	Spatially varying coefficient regression
मौलिक स्रोत	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
उपनाम	Bayesian MGWR, B-MGWR, Bayesian multiscale GWR, Bayesian spatially varying coefficient model	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression
संबंधित	6	6
सारांश	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.	Local Spatial Regression fits a separate regression model at each location in a study area, allowing regression coefficients to vary continuously across space. Rather than forcing one global slope on all observations, it reveals where and how the relationship between predictors and an outcome changes geographically — producing a map of coefficients rather than a single number.