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| 分层自助法模拟× | 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≠ | 1997-2008 | 1960 |
| 提出者≠ | Davison & Hinkley; Cameron, Gelbach & Miller | Rudolf E. Kalman |
| 类型≠ | resampling simulation | recursive Bayesian filter |
| 开创性文献≠ | Davison, A. C. & Hinkley, D. V. (1997). Bootstrap Methods and their Application. Cambridge University Press. ISBN: 978-0521574716 | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ |
| 别名 | cluster bootstrap, multilevel bootstrap, nested bootstrap resampling, hierarchical resampling | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter |
| 相关 | 5 | 5 |
| 摘要≠ | Hierarchical bootstrap simulation is a resampling technique designed for data with nested or clustered structure — students within schools, patients within hospitals, repeated measures within subjects. It preserves the natural grouping of the data by resampling at each level of the hierarchy in sequence, producing a sampling distribution that correctly reflects both between-group and within-group variability. | 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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