방법 비교

	로버스트 베이즈 네트워크	강건 베이즈 추론
분야	베이지안	베이지안
계열	Bayesian methods	Bayesian methods
기원 연도	1991-2000	1984–1990
창시자	Fabio Cozman (credal networks); Peter Walley (imprecise probabilities)	James O. Berger
유형	probabilistic graphical model with set-valued probabilities	Bayesian sensitivity / robustness framework
원전	Cozman, F. G. (2000). Credal networks. Artificial Intelligence, 120(2), 199-233.	Berger, J. O. (1990). Robust Bayesian analysis: sensitivity to the prior. Journal of Statistical Planning and Inference, 25(3), 303–328.
별칭	RBN, credal network, imprecise Bayesian network, sensitivity analysis in Bayesian networks	Bayesian sensitivity analysis, prior robustness, epsilon-contamination Bayesian analysis, robust Bayes
관련	5	6
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.	Robust Bayesian inference extends standard Bayesian analysis by replacing a single prior distribution with a class of plausible priors and examining how much the posterior conclusions change across that class. Instead of committing to one prior, the analyst bounds the posterior quantity of interest, revealing whether findings are stable or critically dependent on prior assumptions.