IMLE:迭代极大似然估计
迭代极大似然估计(Iterative Maximum Likelihood Estimation,简称IMLE)是一种常用的参数估计方法,其名称常缩写为IMLE,以方便书写与交流。该方法通过多次迭代逐步优化似然函数,从而获得更为精确的参数估计结果。IMLE广泛应用于统计学、机器学习及工程学等多个综合领域,是处理复杂模型参数估计问题的有效工具。
Iterative Maximum Likelihood Estimation具体释义
Iterative Maximum Likelihood Estimation的英文发音
例句
- After comparing the advantages and disadvantages of several positioning methods, the author study a modified iterative maximum likelihood estimation algorithm and analyzes its advantages and disadvantages, then a hybrid iterative algorithm is proposed, and compares its location algorithm with the traditional one.
- 研究了一种改进型的迭代最大似然估计算法并分析了它的优缺点,在此基础上提出了一种混合迭代算法,并且将其与传统的定位算法相比较。
- Being modeled as sinusoidal signals, the RFI can be greatly suppressed by the method of iterative Maximum Likelihood ( ML ) estimation.
- 基于多正弦波模型的选代最大似然(ML)法能够在很大程度上抑制RFI。
- Popularly used iterative algorithms, such as maximum likelihood estimation, is limited in estimating the linear structural equation model.
- 利用极大似然法或者最小二乘法等对线性结构方程进行估计时,会受到一定的限制。
- Methods EM algorithm was employed to construct an iterative formula for solving the maximum likelihood estimation ( MLE ) of parameters of Gamma distribution with interval data, whereby we can estimate the distribution parameters of SARS incubation period with interval data.
- 方法采用EM算法构造出求解含区间数据Gamma分布参数极大似然估计的迭代公式,并应用于SARS潜伏期分布的拟合。
- In this approach the Newton iterative method is utilized to solve this maximum likelihood estimation problem, and the initial values are assigned using the Kalman predictor.
- 其中利用牛顿迭代法解决最大似然估计问题,并且利用Kalman预报器为牛顿迭代赋初值。
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