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| Sequentielle Monte-Carlo-Methoden× | Kalman Filter× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 1993 (particle filter); 2006 (SMC samplers) | 1960 |
| Urheber≠ | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) | Rudolf E. Kalman |
| Typ≠ | Sequential Bayesian computation | recursive Bayesian filter |
| Wegweisende Quelle≠ | 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 ↗ |
| Aliasnamen | SMC, particle filter, sequential importance resampling, SMC sampler | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter |
| Verwandt≠ | 6 | 5 |
| Zusammenfassung≠ | 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. |
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