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| Dynamische Variationsinferenz× | Bayessche Inferenz für Zeitreihen× | |
|---|---|---|
| Fachgebiet | Bayes-Statistik | Bayes-Statistik |
| Familie | Bayesian methods | Bayesian methods |
| Entstehungsjahr≠ | 2014–2015 | 1989 |
| Urheber≠ | Bayer, Osendorfer, Krishnan and colleagues | Mike West and Jeff Harrison |
| Typ≠ | Bayesian approximate inference | Bayesian probabilistic model |
| Wegweisende Quelle≠ | Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference. link ↗ | West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259 |
| Aliasnamen | sequential variational inference, temporal variational inference, variational inference for state-space models, DVI | Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS |
| Verwandt | 6 | 6 |
| Zusammenfassung≠ | 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). | Time series Bayesian inference applies Bayes' theorem sequentially to time-ordered observations, maintaining a full probability distribution over hidden states and model parameters at every time step. This framework unifies state-space models, dynamic linear models, and particle filters, producing calibrated uncertainty for both filtering (real-time) and retrospective smoothing tasks. |
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