Linganisha mbinu

	Uchanganuzi wa dhahania wa mfululizo wa wakati	Uchambuzi Sanifu wa Kigeugeu
Nyanja	Mbinu za Bayes	Mbinu za Bayes
Familia	Bayesian methods	Bayesian methods
Mwaka wa asili	1999–2017	2014–2015
Mwanzilishi	Jordan, Ghahramani, Jaakkola, Saul; extended by Blei and colleagues	Bayer, Osendorfer, Krishnan and colleagues
Aina	Approximate Bayesian inference	Bayesian approximate inference
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.	Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference.
Majina mbadala	time-series VI, variational Bayes for time series, TSVI, sequential variational inference	sequential variational inference, temporal variational inference, variational inference for state-space models, DVI
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.	Dynamic variational inference extends the variational inference framework to sequential and time-series settings by positing a structured approximate posterior that respects the temporal ordering of latent states. It jointly learns a generative model of how hidden states evolve over time and a recognition network that maps observed sequences back to those latent states, optimising a sequential evidence lower bound (ELBO).