Bayesian methodsBayesian / computational
空间贝叶斯模型平均
空间贝叶斯模型平均（spatial BMA）将经典BMA扩展到观测值具有地理参考且必须对空间依赖性进行建模的设置。它不是选择一个单一的空间回归模型——例如使用哪个空间权重矩阵，包含哪些回归量，采用哪种空间滞后或误差结构——而是根据每个候选模型在给定数据下的后验概率对其进行加权，然后对所有候选模型的预测和参数估计进行平均。
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来源
- LeSage, J. P. & Pace, R. K. (2009). Introduction to Spatial Econometrics. CRC Press / Taylor & Francis. ISBN: 978-1420064247
- Fernandez, C., Ley, E. & Steel, M. F. J. (2001). Benchmark priors for Bayesian model averaging. Journal of Econometrics, 100(2), 381-427. DOI: 10.1016/S0304-4076(00)00076-2 ↗
如何引用本页
ScholarGate. (2026, June 3). Spatial Bayesian Model Averaging. ScholarGate. https://scholargate.app/zh/bayesian/spatial-bayesian-model-averaging
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 贝叶斯模型平均 (Bayesian Model Averaging, BMA)贝叶斯↔ compare
- Bayesian Regression贝叶斯↔ compare
- 分层贝叶斯推断贝叶斯↔ compare
- 空间贝叶斯推断贝叶斯↔ compare
- Spatial Variational Inference贝叶斯↔ compare