Bayesian methods
经验贝叶斯
经验贝叶斯（EB）是一种估计策略，由Herbert Robbins于1956年提出，并由Bradley Efron和Carl Morris于1973年发展为实用的收缩估计量。在该策略中，先验分布的超参数通过边际似然从观测数据中估计，而不是预先指定。由此产生的后验分布保留了贝叶斯结构，但用数据驱动的超参数取代了主观超参数，从而连接了频率派收缩和完全贝叶斯推断。
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来源
- Robbins, H. (1956). An empirical Bayes approach to statistics. In J. Neyman (Ed.), Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1 (pp. 157–164). University of California Press. DOI: 10.1525/9780520313880-015 ↗
- Efron, B., & Morris, C. (1973). Stein's estimation rule and its competitors — An empirical Bayes approach. Journal of the American Statistical Association, 68(341), 117–130. DOI: 10.1080/01621459.1973.10481350 ↗
- Carlin, B. P., & Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis (2nd ed.). Chapman & Hall/CRC. ISBN: 978-1584881704
- Efron, B., & Hastie, T. (2016). Computer Age Statistical Inference: Algorithms, Evidence, and Data Science. Cambridge University Press. ISBN: 978-1107149892
如何引用本页
ScholarGate. (2026, June 3). Empirical Bayes Estimation. ScholarGate. https://scholargate.app/zh/bayesian/empirical-bayes
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.
- Bayesian Regression贝叶斯↔ compare
- 马尔可夫链蒙特卡洛 (MCMC)贝叶斯↔ compare
- 混合效应模型统计学↔ compare
- 岭回归（Ridge Regression）机器学习↔ compare