Bayesian methodsBayesian / computational
带缺失数据的粒子滤波器
一种适用于某些观测值缺失的状态空间模型的粒子滤波器。该算法使用加权随机样本（粒子）的云来跟踪状态随时间的变化；当某个时间步没有观测值时，仅跳过权重更新步骤，粒子仅通过转移模型向前传播，直到新的数据到达。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Doucet, A., de Freitas, N. & Gordon, N. J. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer, New York. ISBN: 978-0387951461
- Doucet, A., Godsill, S. & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197-208. DOI: 10.1023/A:1008935410038 ↗
如何引用本页
ScholarGate. (2026, June 3). Sequential Monte Carlo Particle Filter for State-Space Models with Missing Observations. ScholarGate. https://scholargate.app/zh/bayesian/particle-filter-with-missing-data
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.
- 缺失数据的贝叶斯推断贝叶斯↔ compare
- 动态粒子滤波器贝叶斯↔ compare
- 带缺失数据的卡尔曼滤波器贝叶斯↔ compare
- 缺失数据下的MCMC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