Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Simulare prin bootstrap spațial× | Monte Carlo Secvențial× | |
|---|---|---|
| Domeniu | Bayesian | Bayesian |
| Familie | Bayesian methods | Bayesian methods |
| Anul apariției≠ | 1990s–2000s | 1993 (particle filter); 2006 (SMC samplers) |
| Autorul original≠ | Lahiri and others, building on Efron's bootstrap (1979) | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Tip≠ | Resampling / simulation | Sequential Bayesian computation |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | spatial block bootstrap, spatial resampling, geostatistical bootstrap, bootstrap for spatial data | SMC, particle filter, sequential importance resampling, SMC sampler |
| Înrudite≠ | 4 | 6 |
| Rezumat≠ | 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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