So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Suy luận biến phân động (Dynamic Variational Inference)× | Bộ lọc hạt (Monte Carlo tuần tự)× | |
|---|---|---|
| Lĩnh vực | Bayes | Bayes |
| Họ | Bayesian methods | Bayesian methods |
| Năm ra đời≠ | 2014–2015 | 1993 |
| Người khởi xướng≠ | Bayer, Osendorfer, Krishnan and colleagues | Gordon, Salmond & Smith |
| Loại≠ | Bayesian approximate inference | Sequential Monte Carlo estimator |
| Công trình gốc≠ | Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference. link ↗ | 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 ↗ |
| Tên gọi khác≠ | sequential variational inference, temporal variational inference, variational inference for state-space models, DVI | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm |
| Liên quan≠ | 6 | 4 |
| Tóm tắt≠ | Dynamic variational inference extends the variational inference framework to sequential and time-series settings by positing a structured approximate posterior that respects the temporal ordering of latent states. It jointly learns a generative model of how hidden states evolve over time and a recognition network that maps observed sequences back to those latent states, optimising a sequential evidence lower bound (ELBO). | 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. |
| ScholarGateBộ dữ liệu ↗ |
|
|