Bayesian methodsBayesian / computational
多层级 MCMC
Multilevel MCMC 应用马尔可夫链蒙特卡洛抽样于贝叶斯层次（多层）模型。它同时从群体层和总体层参数的联合后验分布中抽取样本，跨层传播不确定性，并能够对观测值在群体内共享共同分布特征的聚类或嵌套数据结构进行推断。
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来源
- 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
- Gelfand, A. E. & Smith, A. F. M. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association, 85(410), 398-409. DOI: 10.1080/01621459.1990.10476213 ↗
如何引用本页
ScholarGate. (2026, June 3). Multilevel Markov Chain Monte Carlo. ScholarGate. https://scholargate.app/zh/bayesian/multilevel-mcmc
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.
- Bayesian Regression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
- 变分推断贝叶斯↔ compare