Selected article for: "markov chain and posterior distribution"
Author: Daniel B Larremore; Bailey K Fosdick; Kate M Bubar; Sam Zhang; Stephen M Kissler; C. Jessica E. Metcalf; Caroline Buckee; Yonatan Grad
Title: Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys
  • Document date: 2020_4_20
    • ID: c4cs14ja_62
    Snippet: We sample from the joint posterior distribution inside the integral in Eq. (2) using a Markov chain Monte Carlo (MCMC) algorithm, with univariate Metropolis-Hastings updates. We initialize the age-specific seroprevelance parameters at θ i = (n + + 1)/(n i + 2), setθ equal to the sample mean of the {θ i } and set γ = γ 0 . For each simulation, the MCMC algorithm was run for a total of 50, 100 iterations. The first 100 iterations were discarde.....
    Document: We sample from the joint posterior distribution inside the integral in Eq. (2) using a Markov chain Monte Carlo (MCMC) algorithm, with univariate Metropolis-Hastings updates. We initialize the age-specific seroprevelance parameters at θ i = (n + + 1)/(n i + 2), setθ equal to the sample mean of the {θ i } and set γ = γ 0 . For each simulation, the MCMC algorithm was run for a total of 50, 100 iterations. The first 100 iterations were discarded and every 50th sample was saved to obtain 1, 000 samples from the joint posterior distribution.

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