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	Multilevel Approximate Bayesian Computation	Hierarchiczna inferencja bayesowska
Dziedzina	Statystyka bayesowska	Statystyka bayesowska
Rodzina	Bayesian methods	Bayesian methods
Rok powstania	2000s–2010s	1972 (Lindley & Smith); consolidated 1995–2013
Twórca	Extension of ABC (Beaumont et al., 2002) to multilevel/hierarchical settings; developed across multiple authors in the 2010s	Lindley & Smith; Gelman et al.
Typ	Simulation-based Bayesian inference	Bayesian multilevel model
Źródło pierwotne	Beaumont, M. A., Zhang, W., & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035.	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
Inne nazwy	multilevel ABC, hierarchical ABC, multi-level ABC, ABC for hierarchical models	multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model
Pokrewne	6	6
Podsumowanie	Multilevel Approximate Bayesian Computation (multilevel ABC) extends simulation-based Bayesian inference to hierarchically structured data. When the likelihood is intractable and observations are nested within groups, it replaces direct likelihood evaluation with simulations at each level of the hierarchy, accepting parameter draws whose simulated summary statistics are close to the observed ones.	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.