Bayesian methodsBayesian / computational
鲁棒序贯蒙特卡洛
鲁棒序贯蒙特卡洛（Robust SMC）扩展了标准的粒子滤波，以处理序贯数据中的异常值、重尾噪声和模型失配问题。通过用重尾分布替换高斯似然假设或在粒子加权过程中采用异常值检测策略，即使观测值偏离了假设模型，它也能保持准确的状态跟踪和参数估计。
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来源
- Ristic, B., Arulampalam, S., & Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House. ISBN: 978-1580536318
- Akyildiz, O. D., & Miguez, J. (2020). Nudging the particle filter. Statistics and Computing, 30(2), 315-336. DOI: 10.1007/s11222-019-09884-y ↗
如何引用本页
ScholarGate. (2026, June 3). Robust Sequential Monte Carlo Methods. ScholarGate. https://scholargate.app/zh/bayesian/robust-sequential-monte-carlo
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
- 稳健贝叶斯推断贝叶斯↔ compare
- 鲁棒卡尔曼滤波器 (Robust Kalman Filter)贝叶斯↔ compare
- 顺序蒙特卡洛贝叶斯↔ compare