Porovnať metódy
Prezrite si vybrané metódy vedľa seba; riadky, ktoré sa líšia, sú zvýraznené.
| Priestorové bayesovské priemerovanie modelov× | Bayesovské spriemerovanie modelov× | |
|---|---|---|
| Odbor | Bayesovské metódy | Bayesovské metódy |
| Rodina | Bayesian methods | Bayesian methods |
| Rok vzniku≠ | 2008 | 1999 |
| Tvorca≠ | LeSage & Fischer (building on Raftery et al. BMA framework, 1997) | Hoeting, Madigan, Raftery & Volinsky |
| Typ≠ | Bayesian model combination with spatial structure | Bayesian model averaging |
| Pôvodný zdroj≠ | LeSage, J. P. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247 | Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401. link ↗ |
| Ďalšie názvy≠ | spatial BMA, BMA for spatial data, Bayesian model averaging with spatial effects, spatial model uncertainty averaging | BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA) |
| Príbuzné | 5 | 5 |
| Zhrnutie≠ | 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 Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one. |
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