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| 다수준 근사 베이즈 계산× | 결측값이 있는 베이즈 계층 모델× | |
|---|---|---|
| 분야 | 베이지안 | 베이지안 |
| 계열 | Bayesian methods | Bayesian methods |
| 기원 연도≠ | 2000s–2010s | 1990s–2000s |
| 창시자≠ | Extension of ABC (Beaumont et al., 2002) to multilevel/hierarchical settings; developed across multiple authors in the 2010s | Gelman, Rubin, Little (and collaborators) |
| 유형≠ | Simulation-based Bayesian inference | Bayesian hierarchical model with missing-data integration |
| 원전≠ | 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 |
| 별칭 | multilevel ABC, hierarchical ABC, multi-level ABC, ABC for hierarchical models | BHM missing data, multilevel Bayesian missing data model, hierarchical Bayesian imputation, Bayesian multilevel model with incomplete data |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. | A Bayesian hierarchical model with missing data treats unobserved values as additional unknowns and samples them jointly with all model parameters from the posterior. The nested structure of the hierarchy borrows strength across groups, while the Bayesian framework naturally propagates uncertainty from missingness through every estimate and prediction. |
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