Comparer des méthodes

	Réseau bayésien robuste	Calcul bayésien approximatif
Domaine	Bayésien	Simulation
Famille	Bayesian methods	Process / pipeline
Année d'origine	1991-2000	2002
Auteur d'origine	Fabio Cozman (credal networks); Peter Walley (imprecise probabilities)	—
Type	probabilistic graphical model with set-valued probabilities	Simulation-based Bayesian inference
Source fondatrice	Cozman, F. G. (2000). Credal networks. Artificial Intelligence, 120(2), 199-233.	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.
Alias	RBN, credal network, imprecise Bayesian network, sensitivity analysis in Bayesian networks	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)
Apparentées	5	5
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.	Approximate Bayesian Computation (ABC) is a family of simulation-based inference methods that estimate posterior distributions without requiring an analytically tractable likelihood function. Introduced by Beaumont, Zhang and Balding (2002) in the context of population genetics, ABC replaced the intractable likelihood with repeated model simulation and a comparison of summary statistics between simulated and observed data.