Bayesian methodsBayesian / computational
带缺失数据的Metropolis-Hastings算法
带缺失数据的Metropolis-Hastings算法将未观测值视为潜在变量，并在单个MCMC链中与模型参数一起进行抽样。通过将目标分布扩展到同时包含参数和缺失值，该算法可以在不丢弃不完整案例或不需要单独插补步骤的情况下，产生经过适当校准的后验推断。
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来源
- Tanner, M. A. & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82(398), 528-540. DOI: 10.2307/2289457 ↗
- 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). Metropolis-Hastings Algorithm with Missing Data Augmentation. ScholarGate. https://scholargate.app/zh/bayesian/metropolis-hastings-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.
- 缺失数据的贝叶斯推断贝叶斯↔ compare
- 数据增强 (Data Augmentation)深度学习↔ compare
- 带缺失数据的吉布斯抽样贝叶斯↔ compare
- Hamiltonian Monte Carlo with Missing Data贝叶斯↔ compare
- Metropolis-Hastings算法贝叶斯↔ compare
- Multiple Imputation统计学↔ compare