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| 계층적 파티클 필터× | 파티클 필터 (순차 몬테카를로)× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 2000s–2010s | 1993 |
| 창시자≠ | Briers, Doucet, and colleagues | Gordon, Salmond & Smith |
| 유형≠ | Sequential Monte Carlo / hierarchical state-space inference | Sequential Monte Carlo estimator |
| 원전≠ | 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 ↗ | Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F (Radar and Signal Processing), 140(2), 107–113. DOI ↗ |
| 별칭≠ | nested particle filter, multilevel particle filter, hierarchical SMC, HPF | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm |
| 관련≠ | 5 | 4 |
| 요약≠ | 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 particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive. |
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