Bayesian methodsBayesian / computational
缺失数据下的MCMC
缺失数据下的MCMC是一种贝叶斯计算策略，它将未观测值视为额外的未知参数。通过在从其预测分布中采样缺失值和从其后验分布中采样模型参数之间交替进行，该算法产生一个有效的联合后验分布，该分布完全考虑了缺失引入的不确定性。
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Method map
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来源
- Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860
- 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: 10.1080/01621459.1987.10478458 ↗
如何引用本页
ScholarGate. (2026, June 3). Markov Chain Monte Carlo with Missing Data. ScholarGate. https://scholargate.app/zh/bayesian/mcmc-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.
- 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
- Gibbs Sampling贝叶斯↔ compare
- Hamiltonian Monte Carlo贝叶斯↔ compare
- Metropolis-Hastings算法贝叶斯↔ compare
- Multiple Imputation统计学↔ compare