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时间序列粒子滤波器

时间序列粒子滤波器是一种序贯蒙特卡洛（Sequential Monte Carlo, SMC）方法，用于在新的观测值逐个到来时跟踪非线性、非高斯状态空间模型的隐状态。它将隐状态上不断演化的后验分布表示为一组带权重的随机样本（粒子），并在每个时间步通过传播、似然加权和重采样来更新这些粒子。

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时间序列粒子滤波器
动态贝叶斯网络卡尔曼滤波器粒子滤波器（序贯蒙特卡洛）顺序蒙特卡洛时间序列贝叶斯推断时间序列卡尔曼滤波

来源

  1. 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 ↗
  2. 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). Time Series Particle Filter (Sequential Monte Carlo for State-Space Models). ScholarGate. https://scholargate.app/zh/bayesian/time-series-particle-filter

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ScholarGate — Time series particle filter (Time Series Particle Filter (Sequential Monte Carlo for State-Space Models)). 于 2026-06-15 检索自 https://scholargate.app/zh/bayesian/time-series-particle-filter · 数据集： https://doi.org/10.5281/zenodo.20539026