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| Višerazinska aproksimativna Bayesovska inferencija× | Hijerarhijsko Bayesovo zaključivanje× | |
|---|---|---|
| Područje | Bayesovska statistika | Bayesovska statistika |
| Obitelj | Bayesian methods | Bayesian methods |
| Godina nastanka≠ | 2000s–2010s | 1972 (Lindley & Smith); consolidated 1995–2013 |
| Tvorac≠ | Extension of ABC (Beaumont et al., 2002) to multilevel/hierarchical settings; developed across multiple authors in the 2010s | Lindley & Smith; Gelman et al. |
| Vrsta≠ | Simulation-based Bayesian inference | Bayesian multilevel model |
| Temeljni izvor≠ | 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 |
| Drugi nazivi | multilevel ABC, hierarchical ABC, multi-level ABC, ABC for hierarchical models | multilevel Bayesian modeling, Bayesian hierarchical model, nested Bayesian model, partial pooling model |
| Srodne | 6 | 6 |
| Sažetak≠ | 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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