Research on the Application of Bayesian Statistical Model Based on MCMC Method
To verify the convergence of Bayesian statistical posterior distribution calculation,two classic Bayesian statistical models,namely linear regression and time series,are investigated and constructed in this paper.Based on the Markov Chain Monte Carlo(MCMC)method,the Bayesian linear regression model is validated using the Birdextinct dataset to analyze the relationship between the average extinction time of birds and three variables:average number of nests,population size,and habitat status.Furthermore,based on the series of Consumer Price Index(CPI)from 1982 to 2021,an AR(p)time series forecasting model is established,incorporating differential processing data.The results indicate that both the Bayesian linear regression model and the Bayesian time series forecasting model have converged Markov chains,with the error of only 0.78% for the Bayesian time series forecasting model.The MCMC method can be effectively applied to analysis of Bayesian statistical models.
MCMC methodBayesian statisticsconvergence diagnosistime series forecasting
NETL
NSTL
万方数据
