Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Пространственное моделирование методом Монте-Карло× | Последовательный Монте-Карло× | |
|---|---|---|
| Область | Байесовские методы | Байесовские методы |
| Семейство | Bayesian methods | Bayesian methods |
| Год появления≠ | 1970s–1980s | 1993 (particle filter); 2006 (SMC samplers) |
| Автор метода≠ | B. D. Ripley and the spatial statistics tradition | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Тип≠ | computational simulation | Sequential Bayesian computation |
| Основополагающий источник≠ | Ripley, B. D. (1987). Stochastic Simulation. John Wiley & Sons. ISBN: 978-0471818847 | 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 MC simulation, Monte Carlo spatial analysis, stochastic spatial simulation, spatial stochastic simulation | SMC, particle filter, sequential importance resampling, SMC sampler |
| Связанные≠ | 4 | 6 |
| Сводка≠ | Spatial Monte Carlo simulation applies random sampling methods to spatial problems, generating many stochastic realisations of a spatial process — such as a random field, point pattern, or network — to estimate distributional properties, propagate uncertainty, or test spatial hypotheses. It is a cornerstone technique in geostatistics, spatial epidemiology, ecology, and environmental modelling. | 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 ↗ |
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