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An alternative perspective on the mixture estimation problem

机译：关于混合估计问题的另一种观点

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摘要

The paper presents an alternative perspective on the mixture estimation problem. First, observations are counted into a histogram. Secondly, rough and enhanced parameter estimation followed by the separation of observations is done. Finally, the residue is distributed between the components by the Bayes decision rule. The number of components, the mixture component parameters and the component weights are modelled jointly, no initial parameter estimates are required, the approach is numerically stable, the number of components has no influence upon the convergence and the speed of convergence is very high. The alternative perspective is compared to the EM algorithm and verified through several data sets. The presented algorithm showed significant advantages compared to the competitive methods and has already been successfully applied in reliability and fatigue analyses.
机译：本文提出了关于混合估计问题的另一种观点。首先，将观察值计入直方图。其次，进行粗略和增强的参数估计，然后分离观察值。最后，残留物通过贝叶斯决策规则在各组件之间分配。组分数量，混合组分参数和组分权重被联合建模，不需要初始参数估计，该方法在数值上是稳定的，组分数量对收敛没有影响，并且收敛速度非常高。将替代视角与EM算法进行比较，并通过多个数据集进行验证。与竞争方法相比，该算法显示出显着优势，并且已经成功地应用于可靠性和疲劳分析中。

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