Comparer des méthodes

	Réseau bayésien robuste	Réseau bayésien
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	1991-2000	1988
Auteur d'origine	Fabio Cozman (credal networks); Peter Walley (imprecise probabilities)	Judea Pearl
Type	probabilistic graphical model with set-valued probabilities	Probabilistic graphical model
Source fondatrice	Cozman, F. G. (2000). Credal networks. Artificial Intelligence, 120(2), 199-233.	Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann. ISBN: 978-1558604797
Alias	RBN, credal network, imprecise Bayesian network, sensitivity analysis in Bayesian networks	Bayes network, belief network, probabilistic graphical model, directed graphical model
Apparentées	5	4
Résumé	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.	A Bayesian network is a probabilistic graphical model, introduced by Judea Pearl in 1988, that encodes a set of variables and their conditional dependencies as a directed acyclic graph (DAG). Each node represents a variable; each directed edge encodes a direct probabilistic influence. By combining Bayes' rule with the graph's conditional independence structure, the model supports reasoning under uncertainty — computing the probability of any variable given observed evidence about others.