EM algorithms without missing data.
EM algorithms without missing data.
复制标题
DOI：
10.1191/096228097677258219
复制
发表时间：
1997-03-01
影响因子：
2.3
通讯作者：
Lange, K
中科院分区：
文献类型：
--
作者：
Becker, M P;Yang, I;Lange, K
计算统计学中的大多数问题涉及目标函数的优化，例如对数似然，平方和或对数后验函数。EM算法是最有效的最大化算法之一，因为它迭代地将最大化从复杂函数转移到简单的代理函数。这种理论观点阐明了EM算法的操作，并提出了新的概括。除了简化最大化，优化转移通常会导致高度稳定的算法，具有良好的理解局部和全局收敛特性。虽然收敛可以是极其缓慢的，各种设备存在加速it.Starting与EM算法，我们在本文中回顾了几个优化转移算法的实质性效用在医学统计。
Most problems in computational statistics involve optimization of an objective function such as a loglikelihood, a sum of squares, or a log posterior function. The EM algorithm is one of the most effective algorithms for maximization because it iteratively transfers maximization from a complex function to a simple, surrogate function. This theoretical perspective clarifies the operation of the EM algorithm and suggests novel generalizations. Besides simplifying maximization, optimization transfer usually leads to highly stable algorithms with well-understood local and global convergence properties. Although convergence can be excruciatingly slow, various devices exist for accelerating it. Beginning with the EM algorithm, we review in this paper several optimization transfer algorithms of substantial utility in medical statistics.