期望最大化算法在极大似然模型参数值估计中的应用

J. E. Simarmata
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引用次数: 0

摘要

参数估计是基于总体数据或样本对总体参数值的估计。参数估计可以用几种方法求解,其中一种方法是极大似然法。本研究的重点是基于迭代算法的极大似然估计正态分布数据的参数值。在Matlab 2016a程序的帮助下,将使用的迭代算法是期望最大化算法。结果表明,使用期望最大化算法对印度尼西亚的一个基于年龄组的事故数据的参数和的估估值为,经过2次迭代。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Application of Expectation Maximization Algorithm in Estimating Parameter Values of Maximum Likelihood Model
Parameter estimation is an estimation of the population parameter values ​​based on data or samples of population. Parameter estimation can be solverd by several methods, one of which is the Maximum Likelihood method. The focus of this research is to estimate the parameter value of a normal distribution data with Maximum Likelihood based on iteration algorithm. The iteration algorithm that will be used is the Expectation Maximation Algorithm with help of  Matlab 2016a program. Based on the results obtained that the estimation value of the parameter  and  for an accident data in Indonesia based on age group with using Expectation Maximization algorithm is and  with 2 iterations.
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