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

The mixed distribution model is often used to extract information from heterogeneous 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 estimation 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.