Salīdzināt metodes

	Beijesa ģeogrāfiski svērta regresija (BGWR)	Lokālā telpiskā regresija
Nozare	Telpiskā analīze	Telpiskā analīze
Saime	Regression model	Regression model
Izcelsmes gads	2007	1996
Autors	Wheeler & Calder (2007); Finley (2011)	Brunsdon, Fotheringham & Charlton
Tips	Bayesian spatially varying coefficient regression	Spatially varying coefficient regression
Pirmavots	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.	Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley. ISBN: 978-0471496168
Citi nosaukumi	BGWR, Bayesian GWR, Bayesian spatially varying coefficient model, Bayesian local regression	locally weighted spatial regression, spatially varying coefficient model, local spatial model, place-based regression
Saistītās	5	6
Kopsavilkums	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.	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.