Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Filtre particulaire (Monte Carlo séquentiel)× | Régression bayésienne× | |
|---|---|---|
| Domaine | Bayésien | Bayésien |
| Famille | Bayesian methods | Bayesian methods |
| Année d'origine≠ | 1993 | — |
| Auteur d'origine≠ | Gordon, Salmond & Smith | — |
| Type≠ | Sequential Monte Carlo estimator | Bayesian linear model |
| Source fondatrice≠ | 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 ↗ | 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 |
| Alias≠ | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Apparentées≠ | 4 | 2 |
| Résumé≠ | 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. | 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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