방법 비교

	동적 베이즈 모델 평균화	동적 베이즈 네트워크
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	2010	1989
창시자	Raftery, Karny & Ettler	Thomas Dean & Keiji Kanazawa
유형	dynamic ensemble / model combination	probabilistic graphical model for sequences
원전	Raftery, A. E., Karny, M., & Ettler, P. (2010). Online prediction under model uncertainty via dynamic model averaging: Application to a cold rolling mill. Technometrics, 52(1), 52-66.	Dean, T. & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5(3), 142–150.
별칭	DMA, dynamic model averaging, time-varying BMA, online Bayesian model averaging	DBN, temporal Bayesian network, dynamic probabilistic graphical model, two-slice temporal Bayesian network
관련	6	5
요약	Dynamic Bayesian Model Averaging (DMA) extends standard Bayesian model averaging to settings where the best predictive model may change over time. It maintains a probability distribution over a set of competing models and updates that distribution sequentially as new observations arrive, allowing model weights to evolve rather than remaining fixed across the entire sample.	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.