Linganisha mbinu

	Uchanganuzi wa dhahania wa mfululizo wa wakati	Utohozi wa Kibayesi wa Mfululizo wa Muda
Nyanja	Mbinu za Bayes	Mbinu za Bayes
Familia	Bayesian methods	Bayesian methods
Mwaka wa asili	1999–2017	1989
Mwanzilishi	Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues	Mike West and Jeff Harrison
Aina	Approximate Bayesian inference	Bayesian probabilistic model
Chanzo asilia	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
Majina mbadala	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
Zinazohusiana	6	6
Muhtasari	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.