Porównaj metody

	Wariacyjne wnioskowanie dla szeregów czasowych	Bayesowskie wnioskowanie w szeregach czasowych
Dziedzina	Statystyka bayesowska	Statystyka bayesowska
Rodzina	Bayesian methods	Bayesian methods
Rok powstania	1999–2017	1989
Twórca	Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues	Mike West and Jeff Harrison
Typ	Approximate Bayesian inference	Bayesian probabilistic model
Źródło pierwotne	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
Inne nazwy	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
Pokrewne	6	6
Podsumowanie	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.