Bayesian methodsBayesian / computational
缺失数据下的近似贝叶斯计算
缺失数据下的近似贝叶斯计算（Approximate Bayesian Computation with missing data）将无似然的ABC框架扩展到了观测不完整或部分记录的场景。通过在假定的模型下模拟数据，并接受其模拟的汇总统计量接近观测值的参数抽样，它绕过了计算棘手的似然函数的需要——即使在某些数据值缺失的情况下也是如此。
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来源
- Beaumont, M. A., Zhang, W. & Balding, D. J. (2002). Approximate Bayesian computation in population genetics. Genetics, 162(4), 2025–2035. link ↗
- Rubin, D. B. (1987). Multiple Imputation for Nonresponse in Surveys. John Wiley & Sons. ISBN: 978-0471655749
如何引用本页
ScholarGate. (2026, June 3). Approximate Bayesian Computation with Missing Data. ScholarGate. https://scholargate.app/zh/bayesian/approximate-bayesian-computation-with-missing-data
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 近似贝叶斯计算仿真↔ compare
- 缺失数据的贝叶斯推断贝叶斯↔ compare
- 缺失数据下的MCMC贝叶斯↔ compare
- Multiple Imputation统计学↔ compare
- 粒子滤波器（序贯蒙特卡洛）贝叶斯↔ compare
- 顺序蒙特卡洛贝叶斯↔ compare