Сравнение на методи

	Динамично вариационно извеждане	Калманов филтър
Област	Бейсови методи	Бейсови методи
Семейство	Bayesian methods	Bayesian methods
Година на възникване	2014–2015	1960
Създател	Bayer, Osendorfer, Krishnan and colleagues	Rudolf E. Kalman
Тип	Bayesian approximate inference	recursive Bayesian filter
Основополагащ източник	Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference.	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
Други названия	sequential variational inference, temporal variational inference, variational inference for state-space models, DVI	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
Свързани	6	5
Резюме	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).	The Kalman filter is an optimal recursive algorithm for estimating the hidden state of a linear dynamical system from noisy measurements. At each time step it alternates between a prediction step — projecting the state forward using the system model — and an update step that corrects the prediction with the new observation, producing minimum-variance state estimates and their uncertainty in real time.