방법 비교

	로버스트 베이즈 네트워크	Bayesian Model Averaging
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	1991-2000	1999
창시자	Fabio Cozman (credal networks); Peter Walley (imprecise probabilities)	Hoeting, Madigan, Raftery & Volinsky
유형	probabilistic graphical model with set-valued probabilities	Bayesian model averaging
원전	Cozman, F. G. (2000). Credal networks. Artificial Intelligence, 120(2), 199-233.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.
별칭	RBN, credal network, imprecise Bayesian network, sensitivity analysis in Bayesian networks	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)
관련	5	5
요약	A Robust Bayesian Network extends a classical Bayesian network by replacing each precise conditional probability table with a set of allowable probability distributions — called a credal set. Instead of a single probability for each query, inference returns a range of probabilities, honestly reflecting uncertainty about the model's numeric parameters while preserving the interpretable directed-acyclic-graph structure.	Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one.