Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| Jaringan Bayesian Dinamis× | Monte Carlo Sekuensial× | |
|---|---|---|
| Bidang | Bayesian | Bayesian |
| Keluarga | Bayesian methods | Bayesian methods |
| Tahun asal≠ | 1989 | 1993 (particle filter); 2006 (SMC samplers) |
| Pencetus≠ | Thomas Dean & Keiji Kanazawa | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| Tipe≠ | probabilistic graphical model for sequences | Sequential Bayesian computation |
| Sumber perintis≠ | 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 ↗ |
| Alias | DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network | SMC, particle filter, sequential importance resampling, SMC sampler |
| Terkait≠ | 5 | 6 |
| Ringkasan≠ | 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. |
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