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| Räumliche Bayes'sche Modell-Mittelung× | Bayes'sche Regression× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 2008 | — |
| Urheber≠ | LeSage & Fischer (building on Raftery et al. BMA framework, 1997) | — |
| Typ≠ | Bayesian model combination with spatial structure | Bayesian linear model |
| Wegweisende Quelle≠ | LeSage, J. P. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Aliasnamen≠ | spatial BMA, BMA for spatial data, Bayesian model averaging with spatial effects, spatial model uncertainty averaging | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Verwandt≠ | 5 | 2 |
| Zusammenfassung≠ | Spatial Bayesian model averaging (spatial BMA) extends classical BMA to settings where observations are georeferenced and spatial dependence must be modelled. Rather than selecting a single spatial regression model — which spatial weight matrix to use, which regressors to include, which spatial lag or error structure to adopt — it averages the predictions and parameter estimates across all candidate models, weighting each by its posterior probability given the data. | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. |
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