Krahasoni metodat

	Inferenca Variacionale Dinamike	Inferenca Bajeziane e Serive Kohore
Fusha	Statistika bajesiane	Statistika bajesiane
Familja	Bayesian methods	Bayesian methods
Viti i origjinës	2014–2015	1989
Krijuesi	Bayer, Osendorfer, Krishnan and colleagues	Mike West and Jeff Harrison
Lloji	Bayesian approximate inference	Bayesian probabilistic model
Burimi themelues	Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference.	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Emërtime të tjera	sequential variational inference, temporal variational inference, variational inference for state-space models, DVI	Bayesian time series analysis, Bayesian state-space modeling, probabilistic time series inference, BSTS
Të lidhura	6	6
Përmbledhja	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).	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.