Selected article for: "Poisson sampling process and sampling process"
Author: Erida Gjini
Title: Modeling Covid-19 dynamics for real-time estimates and projections: an application to Albanian data
  • Document date: 2020_3_23
    • ID: ela022bo_9
    Snippet: (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.20.20038141 doi: medRxiv preprint Bayesian inference I used a Bayesian framework to estimate parameters fitting the model to data. We obtain the posterior distributions for θ as: P (θ|data) = P (data|θ) P (data|θ)p(θ)dθ I used flat priors p(θ) for all parameters. The likelihood P (data|θ) is given by a normal distribution for the mean-sq.....
    Document: (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10.1101/2020.03.20.20038141 doi: medRxiv preprint Bayesian inference I used a Bayesian framework to estimate parameters fitting the model to data. We obtain the posterior distributions for θ as: P (θ|data) = P (data|θ) P (data|θ)p(θ)dθ I used flat priors p(θ) for all parameters. The likelihood P (data|θ) is given by a normal distribution for the mean-squared error between the data and the model. We take into account the data d i , f i denoting number of active confirmed cases and cumulative fatalities for each time t i , and compare them with model predictions for I s (t i ) and F (t i ). For simplicity, I do not model the sampling process (e.g. Poisson) in this basic version.

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