结构方程系数矩阵线性约束下的完全信息极大似然估计法。
The first is the full information maximum likelihood method with linear constraint of coefficient matrixes in structure equation.
处理生存分析观测数据使用的参数估计方法有很多，极大似然估计法是最常见的一种估计方法。
There are many methods to deal with measuring data in survival analysis. Maximum likelihood estimation is the most popular one.
进行了新方法、原贝叶斯高分辨方位估计方法与多重信号特征法（MUSIC）和极大似然估计法（MLE）的性能比较研究，揭示了新方法的优越性。
The new method and original Bayesian high-resolution DOA estimator are compared with other typical methods like MUSIC and MLE, and the superiority of the new method is revealed.
为解决这一问题，本文将极大似然法引入其概率图，提出了一种近似的参数估计方法并给出了算例。
To solve this problem, this paper provides a approximate parameter estimating method by using of the Maximum Likelihood method on the probability paper, and a calculation example is offered.
本文用极大拟似然估计法估计了中国银行间市场七天拆借利率扩散模型的参数。
Maximum pseudo-Likelihood method is used to estimate the coefficient functions in the diffusion of Single-Factor Interest Rate Models.
对其中的参数分别用极大似然法和改进的矩法进行了估计。
The distribution parameters have been estimated with either the maximum likelihood method or a modified moment method.
后者既包括基于广义帕雷托分布（GPD）参数的极大似然估计，又包括矩法估计等。
The latter since include maximum likelihood estimate of the GPD(generalized Pareto distribution ) model parameters to still include moment estimate etc.
而且本文通过极大似然法估计小波系数概率模型中的三个参数，使算法达到自适应。
Furthermore, through the maximum likelihood used for evaluating three parameters of wavelet coefficients' probability model, the adaptive algorithm is derived in the article.
研究了广义预测控制的模型反馈校正方法，将递推极大似然法和遗忘因子递推最小二乘法结合起来，给出了一种改进的递推极大似然参数估计算法。
Model feedback correction algorithm of GPC has Benn studied. Combining the RML and forgetting factor RLS, an improved RML parameter estimation method has been given.
应用线性回归技术和极大似然法原理，给出了概率曲线及其置信限的估计方法。
A method for estimating the curves and their confidence bounds is developed by a linear regression technique and a maximum likelihood principle.
与极限平衡法和极大似然法的估计结果进行了比较，可以看出，神经网络方法具有推广预测精度高、自学习功能强、考虑不确定性能力强等特点。
The learned knowledge is then used for extrapolating prediction of the safety factor of new slope, Compared with the safety factors predicted by the limit equilibrium and m…
而且通过极大似然法估计小波系数概率模型中的三个参数，使算法达到自适应性。
Furthermore, by evaluating three parameters of wavelet coefficients "probability model using maximum likelihood method, self adaptively of the algorithm is achieved."
本文对带有右截尾数据的有重复因子试验，提出了另一种分析位置效应和散度效应的方法：首先，在每一个试验点，对重复试验观察值用极大似然法估计出均值和方差；
A method is presented for estimating the location and dispersion effects from these experiments. Firstly, we estimate the variance and the mean of each cell with maximum likelihood;
与极限平衡法和极大似然法的估计结果进行了比较，可以看出，神经网络方法具有推广预测精度高、自学习功能强、考虑不确定性能力强等特点。
The learned knowledge is then used for extrapolating prediction of the safety factor of new slope, Compared with the safety factors predicted by the limit equilibrium and maximum likelihood …
与极限平衡法和极大似然法的估计结果进行了比较，可以看出，神经网络方法具有推广预测精度高、自学习功能强、考虑不确定性能力强等特点。
The learned knowledge is then used for extrapolating prediction of the safety factor of new slope, Compared with the safety factors predicted by the limit equilibrium and maximum likelihood …
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