Bayesian methods
Metropolis-Hastings算法
Metropolis-Hastings (MH)算法是一种通用的马尔可夫链蒙特卡洛 (MCMC) 方法，用于从任何可以评估到归一化常数为止的概率分布中抽取样本。该算法由Metropolis、Rosenbluth、Rosenbluth、Teller和Teller (1953) 在计算物理学中提出，并由Hastings (1970) 推广到非对称提议分布，它是几乎所有后续MCMC采样器（如Gibbs采样、Hamiltonian Monte Carlo、slice sampling）的衍生基础或特例。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
+2 more
来源
- Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21(6), 1087–1092. DOI: 10.1063/1.1699114 ↗
- Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97–109. DOI: 10.1093/biomet/57.1.97 ↗
- Robert, C. P., & Casella, G. (2004). Monte Carlo Statistical Methods (2nd ed.). Springer. ISBN: 978-0-387-21239-5
- 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: 978-1-439-84095-5
如何引用本页
ScholarGate. (2026, June 3). Metropolis-Hastings Markov Chain Monte Carlo Algorithm. ScholarGate. https://scholargate.app/zh/bayesian/metropolis-hastings-algorithm
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
- 顺序蒙特卡洛贝叶斯↔ compare
- 切片采样贝叶斯↔ compare