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| 동적 입자 필터× | 파티클 필터 (순차 몬테카를로)× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도 | 1993 | 1993 |
| 창시자≠ | Gordon, Salmond & Smith (bootstrap particle filter, 1993); extended by Doucet et al. (2001) | Gordon, Salmond & Smith |
| 유형≠ | Sequential Bayesian state estimation | Sequential Monte Carlo estimator |
| 원전≠ | Doucet, A., de Freitas, N. & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer. ISBN: 978-0387951461 | 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≠ | dynamic sequential Monte Carlo, dynamic SMC, bootstrap particle filter, dynamic SIR filter | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm |
| 관련 | 4 | 4 |
| 요약≠ | A dynamic particle filter is a sequential Monte Carlo algorithm that tracks an evolving hidden state over time by maintaining a population of weighted random samples — particles — each representing a plausible trajectory. As new observations arrive, particle weights are updated via the likelihood and the population is resampled, keeping the representation concentrated on the most probable state regions in a fully nonlinear and non-Gaussian setting. | 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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