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	Hierarchical Approximate Bayesian Computation	Przybliżone Obliczenia Bayesa
Dziedzina	Statystyka bayesowska	Symulacja
Rodzina	Bayesian methods	Process / pipeline
Rok powstania	2009–2010	2002
Twórca	Toni, Welch, Strelkowa, Ipsen & Stumpf (building on Pritchard et al. 1999 and Beaumont et al. 2002)	—
Typ	simulation-based Bayesian inference	Simulation-based Bayesian inference
Źródło pierwotne	Toni, T. & Stumpf, M. P. H. (2010). Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics, 26(1), 104–110.	Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035.
Inne nazwy	hierarchical ABC, ABC for hierarchical models, multilevel ABC, population ABC	ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC)
Pokrewne	4	5
Podsumowanie	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.	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.