방법 비교

	시계열 베이즈 계층 모델	동적 베이즈 네트워크
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	1989–1997	1989
창시자	West & Harrison (dynamic models); Gelman et al. (hierarchical Bayesian framework)	Thomas Dean & Keiji Kanazawa
유형	Bayesian hierarchical model for time series	probabilistic graphical model for sequences
원전	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.
별칭	TSBHM, Bayesian hierarchical time series, hierarchical dynamic Bayesian model, multilevel Bayesian time series	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network
관련	6	5
요약	A time series Bayesian hierarchical model combines the hierarchical (multilevel) Bayesian framework with a dynamic state-space structure to analyse temporal data collected on multiple units or groups. Priors encode beliefs about both within-unit dynamics and cross-unit variation, and the posterior is obtained via MCMC or sequential Monte Carlo, yielding full probabilistic forecasts with calibrated uncertainty.	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.