Parameter Estimation of Lognormal Multilayer Bayesian Model
In view of the fact that the traditional normal multi-layer Bayesian mixture model is not effective in the statistical inference of skewed distribution data with mean heterogeneity and variance heterogeneity,this paper combines the construction of log-normal multi-layer prior distribution with Bayesian theorem and introduces it into the mixture model,so as to establish a log-normal multi-layer Bayesian mixture model.In the case of known mixing number(k)of the mixture model,the Bayesian estimation of unknown parameters is performed by applying the Gibbs sampling algorithm,and the convergence of the iterative chain generated by the Gibbs algorithm is diagnosed.Random simulation results show that the effect of Bayesian estimation is better than that of likelihood estimation under the relative error and mean square error(MSE)criteria.Finally,the empirical analysis proves that the established model is feasible.
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