方法对比
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| 带测量误差的近似贝叶斯计算× | 顺序蒙特卡洛× | |
|---|---|---|
| 领域 | 贝叶斯 | 贝叶斯 |
| 方法族 | Bayesian methods | Bayesian methods |
| 起源年份≠ | 2013 (measurement-error extension); ABC: 1997-2002 | 1993 (particle filter); 2006 (SMC samplers) |
| 提出者≠ | Wilkinson, R. D. (formal treatment); ABC roots: Tavaré, Diggle, Beaumont et al. (1997-2002) | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| 类型≠ | likelihood-free Bayesian inference | Sequential Bayesian computation |
| 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 ↗ | Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113. DOI ↗ |
| 别名 | ABC with measurement error, ABC-ME, likelihood-free inference with measurement error, simulation-based inference under measurement error | SMC, particle filter, sequential importance resampling, SMC sampler |
| 相关≠ | 5 | 6 |
| 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. | Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions. |
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