เปรียบเทียบวิธี

	เครือข่ายเบย์เซียนพลวัต	Kalman Filter
สาขาวิชา	เบย์	เบย์
ตระกูล	Bayesian methods	Bayesian methods
ปีกำเนิด	1989	1960
ผู้ริเริ่ม	Thomas Dean & Keiji Kanazawa	Rudolf E. Kalman
ประเภท	probabilistic graphical model for sequences	recursive Bayesian filter
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network	linear quadratic estimator, LQE, Kalman-Bucy filter, optimal recursive filter
ที่เกี่ยวข้อง	5	5
สรุป	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.