Bayesian methodsBayesian / computational
缺失数据贝叶斯模型平均法
缺失数据贝叶斯模型平均法（BMA-MD）同时处理两种不确定性来源：哪个模型最能描述数据，以及未观测值是什么。该方法不是选择单个插补数据集和单个模型，而是跨越候选模型和缺失值合理补全的整个空间对预测进行平均，将这两种不确定性传播到每个估计量和预测中。
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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), 382-417. link ↗
- Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons, New York. ISBN: 978-0471655749
如何引用本页
ScholarGate. (2026, June 3). Bayesian Model Averaging with Missing Data. ScholarGate. https://scholargate.app/zh/bayesian/bayesian-model-averaging-with-missing-data
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.
- 缺失数据下的近似贝叶斯计算贝叶斯↔ compare
- 含缺失数据的贝叶斯分层模型贝叶斯↔ compare
- 缺失数据的贝叶斯推断贝叶斯↔ compare
- 贝叶斯模型平均 (Bayesian Model Averaging, BMA)贝叶斯↔ compare
- Multiple Imputation统计学↔ compare
- 缺失数据的序贯蒙特卡洛方法贝叶斯↔ compare