首页|A unified Minorization-Maximization approach for estimation of general mixture models

A unified Minorization-Maximization approach for estimation of general mixture models

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The mixed distribution model is often used to extract information from heteroge-neous data and perform modeling analysis.When the density function of mixed distribution is complicated or the variable dimension is high,it usually brings challenges to the parameter es-timation of the mixed distribution model.The application of MM algorithm can avoid complex expectation calculations,and can also solve the problem of high-dimensional optimization by decomposing the objective function.In this paper,MM algorithm is applied to the parameter estimation problem of mixed distribution model.The method of assembly and decomposition is used to construct the substitute function with separable parameters,which avoids the problems of complex expectation calculations and the inversion of high-dimensional matrices.

MM algorithmmixed distribution modelparameter estimationassembly decomposition tech-nologyparameter separation

HUANG Xi-fen、LIU Deng-ge、ZHOU Yun-peng、ZHU Fei

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School of Mathematics,Yunnan Normal University,Kunming 650092,China

Department of Statistics and Actuarial Science,The University of Hong Kong,Hong Kong,China

国家自然科学基金General Program of Basic Research Programs of Yunnan ProvinceYunnan Key Laboratory of Modern Analytical Mathematics and ApplicationsCrossintegration Innovation team of modern Applied Mathematics and Life Sciences in Yunnan Province,China

12261108202401AT070126202302AN360007202405AS350003

2024

高校应用数学学报B辑(英文版)
浙江大学 中国工业与应用数学学会

高校应用数学学报B辑(英文版)

影响因子:0.146
ISSN:1005-1031
年,卷(期):2024.39(2)
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