Bayesian methodsBayesian / computational
分层粒子滤波器
分层粒子滤波器将序列蒙特卡洛方法扩展到具有多个潜在变量层级的状态空间模型。粒子在层级的每一层上传播，使该方法能够同时跟踪细粒度的状态动态和变化较慢的超参数，从而在模型的所有层级上产生校准后的后验分布。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Briers, M., Doucet, A. & Maskell, S. (2010). Smoothing algorithms for state-space models. Annals of the Institute of Statistical Mathematics, 62(1), 61-89. DOI: 10.1007/s10463-009-0236-2 ↗
- Chopin, N., Jacob, P. E. & Papaspiliopoulos, O. (2013). SMC2: an efficient algorithm for sequential analysis of state-space models. Journal of the Royal Statistical Society: Series B, 75(3), 397-426. DOI: 10.1111/j.1467-9868.2012.01046.x ↗
如何引用本页
ScholarGate. (2026, June 3). Hierarchical Particle Filter. ScholarGate. https://scholargate.app/zh/bayesian/hierarchical-particle-filter
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.
- 分层贝叶斯推断贝叶斯↔ compare
- 分层马尔可夫链蒙特卡洛贝叶斯↔ compare
- 卡尔曼滤波器贝叶斯↔ compare
- 粒子滤波器（序贯蒙特卡洛）贝叶斯↔ compare
- 顺序蒙特卡洛贝叶斯↔ compare