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	Inferenza variazionale per serie temporali	Inferenza Bayesiana su Serie Storiche
Campo	Bayesiano	Bayesiano
Famiglia	Bayesian methods	Bayesian methods
Anno di origine	1999–2017	1989
Ideatore	Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues	Mike West and Jeff Harrison
Tipo	Approximate Bayesian inference	Bayesian probabilistic model
Fonte seminale	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
Correlati	6	6
Sintesi	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.