方法对比
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| 空间近似贝叶斯计算× | 近似贝叶斯计算× | |
|---|---|---|
| 领域≠ | 贝叶斯 | 仿真 |
| 方法族≠ | Bayesian methods | Process / pipeline |
| 起源年份≠ | 2002 (spatial extensions from mid-2000s) | 2002 |
| 提出者≠ | Diggle & Gratton (implicit statistical models, 1984); Beaumont, Zhang & Balding (ABC formalization, 2002) | — |
| 类型≠ | likelihood-free Bayesian inference | Simulation-based Bayesian inference |
| 开创性文献≠ | Beaumont, M. A., Zhang, W., & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035. DOI ↗ | Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035. DOI ↗ |
| 别名 | Spatial ABC, ABC for spatial data, likelihood-free Bayesian spatial inference, simulation-based spatial inference | ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC) |
| 相关≠ | 4 | 5 |
| 摘要≠ | Spatial Approximate Bayesian Computation (Spatial ABC) is a likelihood-free Bayesian inference framework for spatial data models whose likelihood function is intractable or too expensive to evaluate. It draws candidate parameters from a prior, simulates spatially structured datasets under those parameters, and accepts only the draws whose simulated spatial summary statistics closely match the observed data, thereby building an approximate posterior over model parameters. | 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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