Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Динамическое байесовское усреднение моделей× | Последовательный Монте-Карло× | |
|---|---|---|
| Область | Байесовские методы | Байесовские методы |
| Семейство | Bayesian methods | Bayesian methods |
| Год появления≠ | 2010 | 1993 (particle filter); 2006 (SMC samplers) |
| Автор метода≠ | Raftery, Karny & Ettler | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Тип≠ | dynamic ensemble / model combination | Sequential Bayesian computation |
| Основополагающий источник≠ | Raftery, A. E., Karny, M., & Ettler, P. (2010). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics, 52(1), 52-66. DOI ↗ | 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 | DMA, dynamic model averaging, time-varying BMA, online Bayesian model averaging | SMC, particle filter, sequential importance resampling, SMC sampler |
| Связанные | 6 | 6 |
| Сводка≠ | Dynamic Bayesian Model Averaging (DMA) extends standard Bayesian model averaging to settings where the best predictive model may change over time. It maintains a probability distribution over a set of competing models and updates that distribution sequentially as new observations arrive, allowing model weights to evolve rather than remaining fixed across the entire sample. | 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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