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Bayesiansk multiskala geografisk vektet regresjon

Bayesiansk multiskala geografisk vektet regresjon (Bayesian MGWR) utvider MGWR-rammeverket ved å plassere Bayesianske priorfordelinger på hver romlig varierende koeffisient. Hver prediktor får sin egen båndbredde — sin egen geografiske skala for innflytelse — mens Bayesiansk inferens erstatter klassisk back-fitting med posterior sampling, noe som gir full usikkerhetskvantifisering for hver lokale koeffisientflate.

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  1. Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265. DOI: 10.1080/24694452.2017.1352480
  2. Li, Z., Fotheringham, A. S., Li, W., & Oshan, T. (2020). Fast Geographically Weighted Regression (FastGWR): a scalable algorithm to investigate spatial process heterogeneity in millions of observations. International Journal of Geographical Information Science, 33(1), 155-175. DOI: 10.1080/13658816.2018.1521523

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ScholarGate. (2026, June 3). Bayesian Multiscale Geographically Weighted Regression. ScholarGate. https://scholargate.app/no/spatial-analysis/bayesian-multiscale-geographically-weighted-regression

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ScholarGateBayesian Multiscale Geographically Weighted Regression (Bayesian Multiscale Geographically Weighted Regression). Hentet 2026-06-15 fra https://scholargate.app/no/spatial-analysis/bayesian-multiscale-geographically-weighted-regression · Datasett: https://doi.org/10.5281/zenodo.20539026