Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Simulation par bootstrap spatial× | Monte Carlo séquentiel× | |
|---|---|---|
| Domaine | Bayésien | Bayésien |
| Famille | Bayesian methods | Bayesian methods |
| Année d'origine≠ | 1990s–2000s | 1993 (particle filter); 2006 (SMC samplers) |
| Auteur d'origine≠ | Lahiri and others, building on Efron's bootstrap (1979) | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Type≠ | Resampling / simulation | Sequential Bayesian computation |
| Source fondatrice≠ | Lahiri, S. N. (2003). Resampling Methods for Dependent Data. Springer. ISBN: 978-0387009285 | 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 ↗ |
| Alias | spatial block bootstrap, spatial resampling, geostatistical bootstrap, bootstrap for spatial data | SMC, particle filter, sequential importance resampling, SMC sampler |
| Apparentées≠ | 4 | 6 |
| Résumé≠ | Spatial bootstrap simulation is a resampling technique designed for spatially dependent data. By resampling contiguous spatial blocks rather than independent observations, it preserves the local autocorrelation structure of the data and yields valid estimates of sampling variability for statistics computed on geographic or lattice observations. | 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. |
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