南京航空航天大学学报2017，Vol.49Issue(1)：110-116,7.DOI:10.16356/j.1005-2615.2017.01.017
极大似然最大熵概率密度估计及其优化解法
Estimation and Optimization of MLE Maximum Entropy Probability Density
摘要
Abstract
Aiming at high nonlinearity,low computational accuracy or hard convergence of Lagrangian multiplier calculation in the probability density function estimation by the classic maximum entropy method,a new method combining the maximum likelihood estimation (MLE)maximum entropy probability density method with the sequential updating method is proposed in this paper.Lagrangian optimization function with low nonlinearity is established on the basis of MLE.Furthermore,the sequential updating method is proposed which is constrained by the sample origin moments.Because of unsteady in the process of optimization,the transformation formula of Lagrangian multiplier is deduced again to avoid singularity phenomenon caused by matrix inversion.By analyzing several common distribution and reliability issues using the MLE maximum entropy probability density method and the classic maximum entropy probability density method,it is found that the MLE maximum entropy probability density method has advantage of low nonlinearity and simple form in the optimization function,while the new combination method does well in computational accuracy and optimization convergence.关键词
概率密度估计/可靠性/极大似然估计/最大熵/逐次优化Key words
probability density estimation/reliability/maximum likelihood estimation/maximum entro-py/sequential updating method分类
能源科技引用本文复制引用
吴福仙,温卫东..极大似然最大熵概率密度估计及其优化解法[J].南京航空航天大学学报,2017,49(1):110-116,7.基金项目
国家自然科学基金(51205190)资助项目. （51205190）
