Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Динамічна байєсівська мережа× | Фільтр частинок (послідовний Монте-Карло)× | |
|---|---|---|
| Галузь | Баєсові методи | Баєсові методи |
| Родина | Bayesian methods | Bayesian methods |
| Рік появи≠ | 1989 | 1993 |
| Автор методу≠ | Thomas Dean & Keiji Kanazawa | Gordon, Salmond & Smith |
| Тип≠ | probabilistic graphical model for sequences | Sequential Monte Carlo estimator |
| Основоположне джерело≠ | 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 ↗ |
| Інші назви≠ | DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm |
| Пов'язані≠ | 5 | 4 |
| Підсумок≠ | 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. | The particle filter, introduced by Gordon, Salmond, and Smith in 1993, is a sequential Monte Carlo algorithm that approximates the Bayesian filtering distribution for nonlinear and non-Gaussian state-space models. Rather than tracking a single best estimate, it maintains a cloud of N weighted random samples — particles — that collectively represent the full posterior distribution of a hidden state at each point in time as new observations arrive. |
| ScholarGateНабір даних ↗ |
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