Сравнение на методи

	Вариационен извод за времеви редове	Бейсиански изводи за времеви редове
Област	Бейсови методи	Бейсови методи
Семейство	Bayesian methods	Bayesian methods
Година на възникване	1999–2017	1989
Създател	Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues	Mike West and Jeff Harrison
Тип	Approximate Bayesian inference	Bayesian probabilistic model
Основополагащ източник	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
Други названия	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
Свързани	6	6
Резюме	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.