Salīdzināt metodes

	Bejeziešu daudzmērogo ģeografiski svērto regresiju	Telpiskās nobīdes modelis (SAR / Telpiskais autoregresīvais)
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	2017-2020	1988
Autors	Fotheringham, Yang & Kang (MGWR); Bayesian extension by Li and co-authors	Anselin (textbook formalisation); LeSage & Pace
Tips	Spatially varying coefficient regression	Spatial autoregressive regression
Pirmavots	Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.	Anselin, L. (1988). Spatial Econometrics: Methods and Models. Kluwer Academic.
Citi nosaukumi	Bayesian MGWR, B-MGWR, Bayesian multiscale GWR, Bayesian spatially varying coefficient model	SAR model, spatial autoregressive model, spatial lag, Uzamsal Gecikme Modeli (SAR / Spatial Lag)
Saistītās	6	5
Kopsavilkums	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.	The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and it decomposes spillover effects into direct, indirect, and total impacts.