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带缺失数据的吉布斯抽样

带缺失数据的吉布斯抽样将未观测值视为与模型参数并列的额外未知量，并在马尔可夫链蒙特卡洛循环中联合抽样所有这些量。该方法在给定参数的条件下从其条件分布中抽取缺失值，以及在给定完整数据的条件下从其条件分布中抽取参数之间交替进行，从而同时生成两者的后验分布。

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带缺失数据的吉布斯抽样
含缺失数据的贝叶斯分层模型缺失数据的贝叶斯推断数据增强 (Data Augmentation)Gibbs Sampling缺失数据下的MCMCMultiple Imputation缺失数据时的自助法模拟Hamiltonian Monte Carlo…带缺失数据的Metropolis-Hasting…带缺失数据蒙特卡洛模拟

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来源

  1. 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 ↗
  2. Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley. ISBN: 978-0471183860

如何引用本页

ScholarGate. (2026, June 3). Gibbs Sampling with Missing Data Imputation. ScholarGate. https://scholargate.app/zh/bayesian/gibbs-sampling-with-missing-data

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被引用于

含缺失数据的贝叶斯分层模型缺失数据时的自助法模拟Hamiltonian Monte Carlo with Missing Data带缺失数据的Metropolis-Hastings算法带缺失数据蒙特卡洛模拟缺失数据的序贯蒙特卡洛方法带缺失数据变分推断

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ScholarGate — Gibbs Sampling with Missing Data (Gibbs Sampling with Missing Data Imputation). 于 2026-06-15 检索自 https://scholargate.app/zh/bayesian/gibbs-sampling-with-missing-data · 数据集： https://doi.org/10.5281/zenodo.20539026