Сравнение методов

	Динамический вариационный вывод	Динамическая байесовская сеть
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	2014–2015	1989
Автор метода	Bayer, Osendorfer, Krishnan and colleagues	Thomas Dean & Keiji Kanazawa
Тип	Bayesian approximate inference	probabilistic graphical model for sequences
Основополагающий источник	Krishnan, R. G., Shalit, U., & Sontag, D. (2015). Deep Kalman Filters. NIPS 2015 Workshop on Advances in Approximate Bayesian Inference.	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.
Другие названия	sequential variational inference, temporal variational inference, variational inference for state-space models, DVI	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network
Связанные	6	5
Сводка	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).	A Dynamic Bayesian Network (DBN) extends a standard Bayesian network over time by representing how a set of random variables evolve across discrete time steps. It captures both the conditional independence structure among variables at each instant and the probabilistic dependencies between consecutive time slices, enabling principled reasoning about temporal processes under uncertainty.