Regression model
贝叶斯自助法（Bayesian Bootstrap，由 Rubin 提出）
贝叶斯自助法由 Donald B. Rubin 于 1981 年提出，是一种重采样方法，通过为每个观测值分配从狄利克雷分布（Dirichlet distribution）中抽取的随机权重，来产生频率学派自助法（frequentist bootstrap）的贝叶斯对应物。它能够产生统计量的完整后验分布，并允许纳入先验信息。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Rubin, D. B. (1981). The Bayesian Bootstrap. The Annals of Statistics, 9(1), 130-134. DOI: 10.1214/aos/1176345338 ↗
- Lo, A. Y. (1987). A Large Sample Study of the Bayesian Bootstrap. The Annals of Statistics, 15(1), 360-375. DOI: 10.1214/aos/1176350271 ↗
如何引用本页
ScholarGate. (2026, June 1). Rubin's Bayesian Bootstrap. ScholarGate. https://scholargate.app/zh/statistics/bayesian-bootstrap
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.
- 块自举（移动块和固定块）统计学↔ compare
- Bootstrap Inference统计学↔ compare
- Jackknife Resampling统计学↔ compare
- 置换 (随机化) 检验统计学↔ compare
- Fisher精确随机推断统计学↔ compare