Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Динамическая байесовская сеть× | Последовательный Монте-Карло× | |
|---|---|---|
| Область | Байесовские методы | Байесовские методы |
| Семейство | Bayesian methods | Bayesian methods |
| Год появления≠ | 1989 | 1993 (particle filter); 2006 (SMC samplers) |
| Автор метода≠ | Thomas Dean & Keiji Kanazawa | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Тип≠ | probabilistic graphical model for sequences | Sequential Bayesian computation |
| Основополагающий источник≠ | Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. 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 | DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network | SMC, particle filter, sequential importance resampling, SMC sampler |
| Связанные≠ | 5 | 6 |
| Сводка≠ | A Dynamic Bayesian Network (DBN) extends a standard Bayesian network over time by representing how a set of random variables evolve across discrete time steps. It captures both the conditional independence structure among variables at each instant and the probabilistic dependencies between consecutive time slices, enabling principled reasoning about temporal processes under uncertainty. | 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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