مقایسهٔ روشها
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| فیلتر ذرات پویا× | فیلتر ذرهای (مونت کارلوی ترتیبی)× | |
|---|---|---|
| حوزه | بیزی | بیزی |
| خانواده | Bayesian methods | Bayesian methods |
| سال پیدایش | 1993 | 1993 |
| پدیدآور≠ | Gordon, Salmond & Smith (bootstrap particle filter, 1993); extended by Doucet et al. (2001) | Gordon, Salmond & Smith |
| نوع≠ | Sequential Bayesian state estimation | Sequential Monte Carlo estimator |
| منبع بنیادین≠ | 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 ↗ |
| نامهای دیگر≠ | dynamic sequential Monte Carlo, dynamic SMC, bootstrap particle filter, dynamic SIR filter | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm |
| مرتبط | 4 | 4 |
| خلاصه≠ | 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. |
| ScholarGateمجموعهداده ↗ |
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