Selected article for: "MCMC chain and mean variance"
Author: Hongzhe Zhang; Xiaohang Zhao; Kexin Yin; Yiren Yan; Wei Qian; Bintong Chen; Xiao Fang
Title: Dynamic Estimation of Epidemiological Parameters of COVID-19 Outbreak and Effects of Interventions on Its Spread
  • Document date: 2020_4_6
    • ID: ff4937mj_43
    Snippet: t=2 are generated from model (2) given Θ, Q o 1 and R o 1 . Following the Metropolis-Hastings algorithm (e.g., Geyer and Thompson 34 ), we obtain the estimation of parameters by employing the Markov chain Monte Carlo (MCMC) sampling from (11) . Specifically, suppose Θ (k−1) is the current state of the Markov chain, and let J(Θ | Θ (k−1) ) be the jumping distribution chosen to be independent normals with mean Θ (k−1) and elementwise var.....
    Document: t=2 are generated from model (2) given Θ, Q o 1 and R o 1 . Following the Metropolis-Hastings algorithm (e.g., Geyer and Thompson 34 ), we obtain the estimation of parameters by employing the Markov chain Monte Carlo (MCMC) sampling from (11) . Specifically, suppose Θ (k−1) is the current state of the Markov chain, and let J(Θ | Θ (k−1) ) be the jumping distribution chosen to be independent normals with mean Θ (k−1) and elementwise variance c 2 , where c is a scale parameter for rejection rate adjustment. The MCMC sampling proposes Θ * from J(Θ | Θ (k−1) ) and computes

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