Selected article for: "Markov chain and MCMC method"
Author: Hui Wan; Jing-an Cui; Guo-Jing Yang
Title: Risk estimation and prediction by modeling the transmission of the novel coronavirus (COVID-19) in mainland China excluding Hubei province
  • Document date: 2020_3_6
    • ID: 3e1ji2mw_22
    Snippet: We used the Markov Chain Monte Carlo (MCMC) method with an adaptive Metropolis-Hastings (M-H) algorithm to fit the model. The algorithm is run for 110,000 iterations with a burn-in of the first 80,000 iterations, and the Geweke convergence diagnostic method is employed to assess convergence of chains. At the significance level of 5%(the critical value of z is 1.96), all parameters and initial values estimated do not reject the original hypothesis.....
    Document: We used the Markov Chain Monte Carlo (MCMC) method with an adaptive Metropolis-Hastings (M-H) algorithm to fit the model. The algorithm is run for 110,000 iterations with a burn-in of the first 80,000 iterations, and the Geweke convergence diagnostic method is employed to assess convergence of chains. At the significance level of 5%(the critical value of z is 1.96), all parameters and initial values estimated do not reject the original hypothesis of convergence to a posterior distribution ( Figure 3 ). ⟨ Figure 2 is near here ⟩

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