Application of EM Algorithm in Parameter Estimation of p-Norm Mixture Model

  Objectives  Aiming at the mixed observation data of multiple distribution forms, a expectation-maximum (EM) combined p-norm distributed model(EM_p) is established.

  Methods  Considering that the mixed number in the mixture model belongs to incomplete data, the EM algorithm is introduced to estimate the parameters of the mixture model and the p-model mixture model parameters are derived in detail. The estimated iteration formula and the corresponding iteration steps are given.The mixture Gaussian distribution data, Laplace distribution and Gaussian distribution mixture data, and the residual data of measured global positioning system(GPS) observations are used to verify the correctness and adaptability of the formula in this paper.

  Results and Conclusions  The results of the calculation examples show that, compared with the single probability distribution, the p-norm mixture model can accurately reflect the actual situation of the data distribution, and the model parameters estimated by the EM algorithm have higher accuracy.