Machine learningEstimation
EM算法
期望最大化（EM）算法是一种迭代优化过程，用于在具有潜在变量或缺失数据的统计模型中寻找参数的最大似然估计或最大后验估计。该算法由Dempster、Laird和Rubin在他们1977年的开创性论文中提出，它在计算完整数据对数似然的期望（E步）和最大化该期望关于参数（M步）之间交替进行，保证了每次迭代似然度单调非减。
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来源
- Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–38. DOI: 10.1111/j.2517-6161.1977.tb01600.x ↗
如何引用本页
ScholarGate. (2026, June 2). Expectation-Maximization Algorithm. ScholarGate. https://scholargate.app/zh/statistics/em-algorithm
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