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× | 缺失数据的贝叶斯推断× | |
|---|---|---|
| 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) | 1976–1987 |
| 提出者≠ | Beaumont, Zhang & Balding (ABC); Rubin (missing data framework) | Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation) |
| 类型≠ | likelihood-free Bayesian inference | Bayesian probabilistic model |
| 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 ↗ | Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860 |
| 别名 | ABC with missing data, likelihood-free inference with missing data, simulation-based inference for incomplete data, ABC-MD | Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model |
| 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. | Bayesian inference with missing data treats unobserved values as unknown parameters and integrates them out of the posterior distribution. Rather than deleting or ad hoc imputing incomplete records, the method jointly models observed and missing data under an explicit missing-data mechanism, producing fully calibrated posterior uncertainty that honestly reflects what the data cannot tell us. |
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