We describe the maximum likelihood parameter estimation problem and how the em algorithm can be used for its solution.
描述最大似然参数估计问题，介绍如何用EM算法求解最大似然参数估计。
The HMM parameters were estimated by the EM algorithm.
隐马尔可夫模型参数通过期望最大化算法(EM)来估计。
We further obtain and prove the condition of the correct convergence of the EM algorithm for Gaussian mixtures.
理论分析和数值实验结果表明，高斯混合密度的EM算法的正确收敛性与混合密度的重叠度密切相关。
After explaining the EM algorithm, this paper gives the derivation of the multi-user detection algorithm based on the EM method.
在讨论了EM算法的基本原理之后，本文详细推导了基于EM方法的多用户检测算法。
We calculate the ML estimation via the EM algorithm, and derive its iteration equations, which gives a closed-form solution for parameters.
我们基于EM算法来计算参数的ML估计，推导了对应的参数迭代方程，给出了参数的一个闭式解。
Especially, we give some results of the convergence of the EM algorithm for the curved exponential family under the conditions checked easily.
特别对应用广泛的曲指数族，本文在较易实际验证的条件下给出了相应的EM算法的收敛结果。
In this paper we discuss the convergence of the EM algorithm for iterative computation of maximum likelihood estimates when the observations can be viewed as incomplete data.
本文讨论EM算法的收敛性，其中EM算法是不完全数据处理中的一类重要的参数估计的迭代算法。
First we introduce the abstract form of the EM algorithm. Then we develop the EM parameter estimation procedure for one application: finding the parameters of a mixture of Gaussian densities.
首先给出em算法的抽象形式，然后研究EM参数估计方法的一个应用：求高斯混合密度的参数。
First we introduce the abstract form of the EM algorithm. Then we develop the EM parameter estimation procedure for one application: finding the parameters of a mixture of Gaussian densities.
首先给出em算法的抽象形式，然后研究EM参数估计方法的一个应用：求高斯混合密度的参数。
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