Bayesian methodsBayesian / computational
缺失数据的序贯蒙特卡洛方法
缺失数据的序贯蒙特卡洛（Sequential Monte Carlo, SMC）方法将标准粒子滤波器扩展到某些观测值缺失的状态空间模型。当某个时间步的观测值缺失时，更新步骤被简单地跳过：粒子通过转移模型向前传播，不进行重新加权，只要缺失是可忽略的（随机缺失或完全随机缺失），就能在任何缺失数据模式下保持精确的贝叶斯推断。
阅读完整方法
仅限会员
登录使用免费账户登录即可阅读本节。
Method map
The neighbourhood of related methods — select a node to explore.
来源
- Doucet, A., de Freitas, N., & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer, New York. ISBN: 978-0387951461
- Chopin, N., & Papaspiliopoulos, O. (2020). An Introduction to Sequential Monte Carlo. Springer, Cham. DOI: 10.1007/978-3-030-47845-2 ↗
如何引用本页
ScholarGate. (2026, June 3). Sequential Monte Carlo with Missing Data. ScholarGate. https://scholargate.app/zh/bayesian/sequential-monte-carlo-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
- 动态序贯蒙特卡洛法贝叶斯↔ compare
- 带缺失数据的吉布斯抽样贝叶斯↔ compare
- 带缺失数据的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