Selected article for: "MCMC algorithm and posterior distribution"

Author: Lionel Roques; Etienne Klein; Julien Papaix; Samuel Soubeyrand
Title: Mechanistic-statistical SIR modelling for early estimation of the actual number of cases and mortality rate from COVID-19
  • Document date: 2020_3_24
  • ID: dqg8fkca_19
    Snippet: where π(α, t 0 , κ) corresponds to the prior distribution of the parameters (therefore uniform) and C is a normalization constant independent of the parameters. The numerical computation of the posterior distribution is performed with a Metropolis-Hastings (MCMC) algorithm, using 4 independent chains, each of which with 10 6 iterations, starting from random values close to the MLE. The dataδ t used to compute the MLE and the posterior distrib.....
    Document: where π(α, t 0 , κ) corresponds to the prior distribution of the parameters (therefore uniform) and C is a normalization constant independent of the parameters. The numerical computation of the posterior distribution is performed with a Metropolis-Hastings (MCMC) algorithm, using 4 independent chains, each of which with 10 6 iterations, starting from random values close to the MLE. The dataδ t used to compute the MLE and the posterior distribution are those corresponding to the period from February 29 to March 17.

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