方法对比
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| 带测量误差的近似贝叶斯计算× | 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≠ | 2013 (measurement-error extension); ABC: 1997-2002 | 2002 |
| 提出者≠ | Wilkinson, R. D. (formal treatment); ABC roots: Tavaré, Diggle, Beaumont et al. (1997-2002) | — |
| 类型≠ | likelihood-free Bayesian inference | Simulation-based Bayesian inference |
| 开创性文献≠ | Wilkinson, R. D. (2013). Approximate Bayesian computation (ABC) gives exact results under the assumption of model error. Statistical Applications in Genetics and Molecular Biology, 12(2), 129-141. DOI ↗ | Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035. DOI ↗ |
| 别名 | ABC with measurement error, ABC-ME, likelihood-free inference with measurement error, simulation-based inference under measurement error | ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC) |
| 相关 | 5 | 5 |
| 摘要≠ | Approximate Bayesian Computation with measurement error (ABC-ME) extends the standard ABC likelihood-free framework to settings where observed data are themselves noisy or imprecisely recorded. By explicitly incorporating a measurement-error kernel into the acceptance step, ABC-ME targets the correct posterior over model parameters even when the true data-generating process cannot be directly observed. | 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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