Bayesian methodsBayesian / computational
多层吉布斯采样
多层吉布斯采样将吉布斯马尔可夫链蒙特卡洛 (MCMC) 算法应用于贝叶斯分层（多层）模型，依次循环遍历组级别参数和总体级别超参数的条件分布。这利用了分层的条件独立结构，从原本在解析上难以处理的后验分布中抽取精确或近乎精确的样本。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Gelman, A. & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN: 978-0521686891
- 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 Gibbs Sampling for Hierarchical Bayesian Models. ScholarGate. https://scholargate.app/zh/bayesian/multilevel-gibbs-sampling
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
- Gibbs Sampling贝叶斯↔ compare
- Hamiltonian Monte Carlo贝叶斯↔ compare
- 分层贝叶斯推断贝叶斯↔ compare
- Metropolis-Hastings算法贝叶斯↔ compare
- 多层级 MCMC贝叶斯↔ compare