Comparer des méthodes

	Calcul bayésien approximatif hiérarchique	Inférence bayésienne hiérarchique
Domaine	Bayésien	Bayésien
Famille	Bayesian methods	Bayesian methods
Année d'origine	2009–2010	1972 (Lindley & Smith); consolidated 1995–2013
Auteur d'origine	Toni, Welch, Strelkowa, Ipsen & Stumpf (building on Pritchard et al. 1999 and Beaumont et al. 2002)	Lindley & Smith; Gelman et al.
Type	simulation-based Bayesian inference	Bayesian multilevel model
Source fondatrice	Toni, T. & Stumpf, M. P. H. (2010). Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics, 26(1), 104–110.	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
Alias	hierarchical ABC, ABC for hierarchical models, multilevel ABC, population ABC	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model
Apparentées	4	6
Résumé	Hierarchical ABC is a likelihood-free Bayesian inference method designed for multilevel data structures in which individual-level parameters are themselves drawn from a population-level distribution. By combining simulation-based rejection sampling with hierarchical pooling, it recovers both within-group and between-group posterior distributions without requiring a tractable likelihood function.	Hierarchical Bayesian inference is a probabilistic modeling framework that organises parameters into levels, placing priors on the group-level parameters and hyperpriors on the parameters governing those priors. It enables partial pooling of information across groups, balancing the extremes of treating each group as independent or merging them into a single estimate.