Bayesian methodsBayesian / computational
动态贝叶斯模型平均
动态贝叶斯模型平均（DMA）将标准的贝叶斯模型平均扩展到最佳预测模型可能随时间变化的场景。它在多个竞争模型上维护一个概率分布，并随着新观测值的到来顺序更新该分布，从而允许模型权重在整个样本期间演变而不是保持固定。
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来源
- Raftery, A. E., Karny, M., & Ettler, P. (2010). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics, 52(1), 52-66. DOI: 10.1198/TECH.2009.08104 ↗
- Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-401. link ↗
如何引用本页
ScholarGate. (2026, June 3). Dynamic Bayesian Model Averaging. ScholarGate. https://scholargate.app/zh/bayesian/dynamic-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
- 动态贝叶斯推断贝叶斯↔ compare
- 动态贝叶斯网络贝叶斯↔ compare
- 动态变分推断贝叶斯↔ compare
- 卡尔曼滤波器贝叶斯↔ compare
- 顺序蒙特卡洛贝叶斯↔ compare