Bayesian methodsBayesian / computational
带缺失数据的卡尔曼滤波器
带缺失数据的卡尔曼滤波器是对经典卡尔曼滤波器在处理部分观测值缺失的时间序列时的扩展。当某个时刻 t 的观测值缺失时，更新步骤将被跳过，状态估计仅由预测步骤向前传递。结合期望最大化 (EM) 算法，该方法还可以从不完整数据中估计未知的模型参数，使其成为处理现实世界中不规则观测序列的实用工具。
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来源
- Shumway, R. H. & Stoffer, D. S. (2000). Time Series Analysis and Its Applications. Springer. ISBN: 978-0387989501
- Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. ISBN: 978-0521405737
如何引用本页
ScholarGate. (2026, June 3). Kalman Filter for State-Space Models with Missing Observations. ScholarGate. https://scholargate.app/zh/bayesian/kalman-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
- EM算法统计学↔ compare
- 卡尔曼滤波器贝叶斯↔ compare
- 带缺失数据的粒子滤波器贝叶斯↔ compare
- 顺序蒙特卡洛贝叶斯↔ compare
- 状态空间模型（卡尔曼滤波器）计量经济学↔ compare