Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Пространственное бутстрэп-моделирование× | Последовательный Монте-Карло× | |
|---|---|---|
| Область | Байесовские методы | Байесовские методы |
| Семейство | Bayesian methods | Bayesian methods |
| Год появления≠ | 1990s–2000s | 1993 (particle filter); 2006 (SMC samplers) |
| Автор метода≠ | Lahiri and others, building on Efron's bootstrap (1979) | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Тип≠ | Resampling / simulation | Sequential Bayesian computation |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия | spatial block bootstrap, spatial resampling, geostatistical bootstrap, bootstrap for spatial data | SMC, particle filter, sequential importance resampling, SMC sampler |
| Связанные≠ | 4 | 6 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
|
|