Comparer des méthodes

	Inférence variationnelle pour séries temporelles	Inférence bayésienne sur séries temporelles
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1999–2017	1989
Auteur d'origine	Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues	Mike West and Jeff Harrison
Type	Approximate Bayesian inference	Bayesian probabilistic model
Source fondatrice	Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859-877.	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Alias	time-series VI, variational Bayes for time series, TSVI, sequential variational inference	Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS
Apparentées	6	6
Résumé	Time series variational inference applies variational Bayes to sequential data, approximating the intractable posterior over latent states and parameters with a tractable family of distributions. By maximising the evidence lower bound (ELBO), it delivers fast, scalable Bayesian inference for state-space models, dynamic latent variable models, and other time-ordered probabilistic systems.	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.