基于高斯模型的差分进化模体的进化学习

Peng Guo, Naixiang Li, Tonghai Liu
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引用次数: 0

摘要

在本文中,我们提出了一种生物聚合物序列中基序的进化学习方法。本文重点研究了高斯模型的进化推理,将微分进化算法用于优化和马尔可夫链蒙特卡罗(MCMC)方法用于抽样的高斯模型概率学习。该框架包括相应权重、均值和协方差的计算。为了获得满意的MCMC采样效果,讨论了MCMC比率的适应度函数。通过对差分进化和差分进化MCMC结果的比较,显示了我们的方法在合成数据集和真实数据集上的新效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Notice of RetractionEvolutionary learning of Gaussian model for motifs with differential evolution MCMC
In this paper, we present an approach for evolutionary learning of motif in biopolymer sequences. The focuses in this paper is evolutionary inference of Gaussian model, Differential Evolution for optimization and Markov chain Monte Carlo(MCMC) for sampling are applied in the probability learning of Gaussian model. The framework involves calculations of corresponding weight, mean and covariance. To obtain satisfied effect of MCMC sampling, the fitness function is discussed for MCMC ratio. Comparisons between results of Differential Evolution and Differential Evolution MCMC are provided to show novel effect of our method on synthetic dataset and real world dataset.
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