方法对比
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| 分层粒子滤波器× | 卡尔曼滤波器× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 2000s–2010s | 1960 |
| 提出者≠ | Briers, Doucet, and colleagues | Rudolf E. Kalman |
| 类型≠ | Sequential Monte Carlo / hierarchical state-space inference | recursive Bayesian filter |
| 开创性文献≠ | Briers, M., Doucet, A. & Maskell, S. (2010). Smoothing algorithms for state-space models. Annals of the Institute of Statistical Mathematics, 62(1), 61-89. DOI ↗ | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ |
| 别名 | nested particle filter, multilevel particle filter, hierarchical SMC, HPF | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter |
| 相关 | 5 | 5 |
| 摘要≠ | A hierarchical particle filter extends Sequential Monte Carlo to state-space models with multiple levels of latent variables. Particles are propagated at each level of the hierarchy, allowing the method to track both fine-grained state dynamics and slower-varying hyperparameters simultaneously, yielding calibrated posterior distributions across all levels of the model. | The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time. |
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