Сравнение методов

	Динамическая байесовская сеть	Фильтр Калмана
Область	Байесовские методы	Байесовские методы
Семейство	Bayesian methods	Bayesian methods
Год появления	1989	1960
Автор метода	Thomas Dean & Keiji Kanazawa	Rudolf E. Kalman
Тип	probabilistic graphical model for sequences	recursive Bayesian filter
Основополагающий источник	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.	Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82(1), 35-45.
Другие названия	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
Связанные	5	5
Сводка	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.	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.