Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Фильтр Калмана× | Байесовская регрессия× | |
|---|---|---|
| Область | Байесовские методы | Байесовские методы |
| Семейство | Bayesian methods | Bayesian methods |
| Год появления≠ | 1960 | — |
| Автор метода≠ | Rudolf E. Kalman | — |
| Тип≠ | recursive Bayesian filter | Bayesian linear model |
| Основополагающий источник≠ | Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45. DOI ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| Другие названия≠ | linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Связанные≠ | 5 | 2 |
| Сводка≠ | 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. | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. |
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