Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Monte Carlo Sequencial para Séries Temporais× | Rede Bayesiana Dinâmica× | |
|---|---|---|
| Área | Bayesiano | Bayesiano |
| Família | Bayesian methods | Bayesian methods |
| Ano de origem≠ | 1993 | 1989 |
| Autor original≠ | Gordon, Salmond & Smith | Thomas Dean & Keiji Kanazawa |
| Tipo≠ | Sequential Bayesian filtering algorithm | probabilistic graphical model for sequences |
| Fonte seminal≠ | 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 ↗ | Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150. DOI ↗ |
| Outros nomes | particle filter, time series SMC, sequential particle filtering, bootstrap particle filter | DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network |
| Relacionados | 5 | 5 |
| Resumo≠ | Time series sequential Monte Carlo (SMC), commonly called the particle filter, is a Bayesian simulation method that tracks the hidden state of a dynamical system as observations arrive one at a time. A cloud of weighted random samples — particles — is propagated forward through the system dynamics, reweighted by how well each particle explains the new observation, and periodically resampled to keep the representation concentrated on plausible states. | 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. |
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