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| 계층적 근사 베이즈 계산× | 근사 베이즈 계산× | |
|---|---|---|
| 분야≠ | 베이지안 | 시뮬레이션 |
| 계열≠ | Bayesian methods | Process / pipeline |
| 기원 연도≠ | 2009–2010 | 2002 |
| 창시자≠ | Toni, Welch, Strelkowa, Ipsen & Stumpf (building on Pritchard et al. 1999 and Beaumont et al. 2002) | — |
| 유형 | simulation-based Bayesian inference | Simulation-based Bayesian inference |
| 원전≠ | Toni, T. & Stumpf, M. P. H. (2010). Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics, 26(1), 104–110. DOI ↗ | Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035. DOI ↗ |
| 별칭 | hierarchical ABC, ABC for hierarchical models, multilevel ABC, population ABC | ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC) |
| 관련≠ | 4 | 5 |
| 요약≠ | 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. |
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