Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Послідовний Монте-Карло× | Фільтр Калмана× | |
|---|---|---|
| Галузь | Баєсові методи | Баєсові методи |
| Родина | Bayesian methods | Bayesian methods |
| Рік появи≠ | 1993 (particle filter); 2006 (SMC samplers) | 1960 |
| Автор методу≠ | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) | Rudolf E. Kalman |
| Тип≠ | Sequential Bayesian computation | recursive Bayesian filter |
| Основоположне джерело≠ | 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 ↗ | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ |
| Інші назви | SMC, particle filter, sequential importance resampling, SMC sampler | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter |
| Пов'язані≠ | 6 | 5 |
| Підсумок≠ | Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions. | 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. |
| ScholarGateНабір даних ↗ |
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