Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Dynamisk variell inferens× | Partikelfilter (sekventiell Monte Carlo)× | |
|---|---|---|
| Ämnesområde | Bayesiansk statistik | Bayesiansk statistik |
| Familj | Bayesian methods | Bayesian methods |
| Ursprungsår≠ | 2014–2015 | 1993 |
| Upphovsperson≠ | Bayer, Osendorfer, Krishnan and colleagues | Gordon, Salmond & Smith |
| Typ≠ | Bayesian approximate inference | Sequential Monte Carlo estimator |
| Ursprungskälla≠ | 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 ↗ |
| Alias≠ | sequential variational inference, temporal variational inference, variational inference for state-space models, DVI | SMC, sequential Monte Carlo, bootstrap filter, condensation algorithm |
| Närliggande≠ | 6 | 4 |
| Sammanfattning≠ | 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. |
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