Comparer des méthodes

	Inférence variationnelle dynamique	Inférence bayésienne sur séries temporelles
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	2014–2015	1989
Auteur d'origine	Bayer, Osendorfer, Krishnan and colleagues	Mike West and Jeff Harrison
Type	Bayesian approximate inference	Bayesian probabilistic model
Source fondatrice	Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference.	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Alias	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
Apparentées	6	6
Résumé	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.