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| Многостепенна приближена байесова компютация× | Йерархично Бейсианско заключение× | |
|---|---|---|
| Област | Бейсови методи | Бейсови методи |
| Семейство | Bayesian methods | Bayesian methods |
| Година на възникване≠ | 2000s–2010s | 1972 (Lindley & Smith); consolidated 1995–2013 |
| Създател≠ | Extension of ABC (Beaumont et al., 2002) to multilevel/hierarchical settings; developed across multiple authors in the 2010s | Lindley & Smith; Gelman et al. |
| Тип≠ | Simulation-based Bayesian inference | Bayesian multilevel model |
| Основополагащ източник≠ | Beaumont, M. A., Zhang, W., & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035. DOI ↗ | 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 |
| Други названия | multilevel ABC, hierarchical ABC, multi-level ABC, ABC for hierarchical models | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| Свързани | 6 | 6 |
| Резюме≠ | 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. |
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