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首页> 外文期刊>Research journal of applied science, engineering and technology >Optimal Penalty Functions Based on MCMC for Testing Homogeneity of Mixture Models
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Optimal Penalty Functions Based on MCMC for Testing Homogeneity of Mixture Models

机译:基于MCMC的最佳惩罚功能,用于测试混合模型的均匀性

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摘要

This study is intended to provide an estimation of penalty function for testing homogeneity of mixture models based on Markov chain Monte Carlo simulation. The penalty function is considered as a parametric function and parameter of determinative shape of the penalty function in conjunction with parameters of mixture models are estimated by a Bayesian approach. Different mixture of uniform distribution are used as prior. Some simulation examples are perform to confirm the efficiency of the present work in comparison with the previous approaches.
机译:本研究旨在提供基于马尔可夫链蒙特卡罗模拟的混合模型的均匀性的惩罚功能的估计。惩罚函数被认为是参数函数,并且结合混合模型的参数的惩罚功能的确定形状参数估计是由贝叶斯方法估算的。用作不同的均匀分布混合物。一些模拟实施例是为了确认与先前方法相比的本工作的效率。

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