方法对比
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| 时间序列近似贝叶斯计算× | 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≠ | 2009 | 2002 |
| 提出者≠ | Beaumont, Zhang & Balding (2002) for ABC; Toni et al. (2009) for dynamical/time-series extension | — |
| 类型≠ | likelihood-free Bayesian inference | Simulation-based Bayesian inference |
| 开创性文献≠ | Toni, T., Welch, D., Strelkowa, N., Ipsen, A. & Stumpf, M. P. H. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface, 6(31), 187–202. DOI ↗ | Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035. DOI ↗ |
| 别名 | TS-ABC, time series ABC, likelihood-free inference for time series, ABC for dynamical systems | ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC) |
| 相关≠ | 6 | 5 |
| 摘要≠ | Time series ABC is a likelihood-free Bayesian inference method that estimates the posterior distribution of model parameters for dynamical or time-indexed systems by comparing summary statistics of simulated trajectories to those of the observed series, bypassing the need to evaluate an analytic likelihood. It is particularly valuable for complex mechanistic or stochastic models whose likelihoods are intractable. | 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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