Bayesian methodsBayesian / computational
时间序列序列蒙特卡洛方法
时间序列序列蒙特卡洛方法（SMC），通常称为粒子滤波器，是一种贝叶斯模拟方法，它在观测值逐个到来时跟踪动态系统的隐藏状态。一组加权的随机样本——粒子——通过系统动力学向前传播，根据每个粒子对新观测值的解释程度进行重新加权，并定期重采样以使表示集中在合理的可能状态上。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F — Radar and Signal Processing, 140(2), 107–113. DOI: 10.1049/ip-f-2.1993.0015 ↗
- Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer. ISBN: 978-0387951461
如何引用本页
ScholarGate. (2026, June 3). Sequential Monte Carlo Methods for Time Series. ScholarGate. https://scholargate.app/zh/bayesian/time-series-sequential-monte-carlo
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.
- 动态贝叶斯网络贝叶斯↔ compare
- Gibbs Sampling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