Bayesian methodsBayesian / computational
稳健粒子滤波器
鲁棒粒子滤波器是一种序贯蒙特卡洛方法，用于在非线性、非高斯系统中跟踪隐藏状态，同时对异常值和模型误设定保持鲁棒性。它用重尾或有界影响密度取代了标准高斯似然函数，从而使异常观测值的重要性降低，避免了状态估计的偏离。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Ristic, B., Arulampalam, S. & Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. ISBN: 978-1580536318
- Hurzeler, M. & Kunsch, H. R. (1998). Monte Carlo approximations for general state-space models. Journal of Computational and Graphical Statistics, 7(2), 175-193. link ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Particle Filter. ScholarGate. https://scholargate.app/zh/bayesian/robust-particle-filter
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.
- Hamiltonian Monte Carlo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
- 鲁棒卡尔曼滤波器 (Robust Kalman Filter)贝叶斯↔ compare
- 鲁棒序贯蒙特卡洛贝叶斯↔ compare
- 顺序蒙特卡洛贝叶斯↔ compare