Bayesian methodsBayesian / computational
多层贝叶斯模型平均
多层贝叶斯模型平均（ML-BMA）将经典的贝叶斯模型平均扩展到分组或分层结构数据。它不依赖于单一的多层模型规范，而是计算一组候选多层模型预测和参数估计的加权平均值，每个模型的权重由其在给定数据下的后验概率决定。结果同时考虑了分组结构、固定效应、随机效应和协变量选择方面的不确定性。
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Method map
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来源
- Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382-401. link ↗
- 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
如何引用本页
ScholarGate. (2026, June 3). Multilevel Bayesian Model Averaging. ScholarGate. https://scholargate.app/zh/bayesian/multilevel-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
- Gibbs Sampling贝叶斯↔ compare
- 分层贝叶斯推断贝叶斯↔ compare
- 多层级 MCMC贝叶斯↔ compare
- 多层变分推断贝叶斯↔ compare