方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 缺失数据下的近似贝叶斯计算× | 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≠ | 2002 (ABC); 1987 (missing data theory) | 1993 (particle filter); 2006 (SMC samplers) |
| 提出者≠ | Beaumont, Zhang & Balding (ABC); Rubin (missing data framework) | 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≠ | Beaumont, M. A., Zhang, W. & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035. link ↗ | 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 missing data, likelihood-free inference with missing data, simulation-based inference for incomplete data, ABC-MD | SMC, particle filter, sequential importance resampling, SMC sampler |
| 相关 | 6 | 6 |
| 摘要≠ | Approximate Bayesian Computation with missing data extends the likelihood-free ABC framework to settings where observations are incomplete or partially recorded. By simulating data under a posited model and accepting parameter draws whose simulated summary statistics are close to the observed ones, it bypasses the need to evaluate an intractable likelihood — even when some data values are absent. | 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. |
| ScholarGate数据集 ↗ |
|
|