Bayesian methodsBayesian / computational
动态 Metropolis-Hastings 算法
动态 Metropolis-Hastings (Dynamic MH) 算法将 Metropolis-Hastings MCMC 采样器应用于贝叶斯状态空间和时变参数模型。在每个时间步，通过提议-接受（proposal-and-accept）步骤更新潜在状态或演化参数，从而获得轨迹的完整后验分布，而非单一的滤波估计。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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 ↗
- Carlin, B. P., Polson, N. G., & Stoffer, D. S. (1992). A Monte Carlo approach to nonnormal and nonlinear state-space modeling. Journal of the American Statistical Association, 87(418), 493–500. DOI: 10.1080/01621459.1992.10475231 ↗
如何引用本页
ScholarGate. (2026, June 3). Dynamic Metropolis-Hastings Algorithm for Time-Varying Models. ScholarGate. https://scholargate.app/zh/bayesian/dynamic-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.
- 动态贝叶斯推断贝叶斯↔ compare
- Gibbs Sampling贝叶斯↔ compare
- 卡尔曼滤波器贝叶斯↔ compare
- Metropolis-Hastings算法贝叶斯↔ compare
- 粒子滤波器（序贯蒙特卡洛）贝叶斯↔ compare