Selected article for: "bayesian approach and posterior distribution"

Author: Nicolas Banholzer; Eva van Weenen; Bernhard Kratzwald; Arne Seeliger; Daniel Tschernutter; Pierluigi Bottrighi; Alberto Cenedese; Joan Puig Salles; Stefan Feuerriegel; Werner Vach
Title: Estimating the impact of non-pharmaceutical interventions on documented infections with COVID-19: A cross-country analysis
  • Document date: 2020_4_21
  • ID: mds06a8i_36
    Snippet: The model parameters are estimated by a fully Bayesian approach with weakly informative priors, except for restricting the effect parameters θ m a priori to non-positive values (i.e., NPIs can only reduce the number of new cases). Details, including the computational approach, are given in supplements. We report for each measure m the posterior distribution of 1 − exp(θ m ), that is, the relative reduction in new cases. 9 All rights reserved......
    Document: The model parameters are estimated by a fully Bayesian approach with weakly informative priors, except for restricting the effect parameters θ m a priori to non-positive values (i.e., NPIs can only reduce the number of new cases). Details, including the computational approach, are given in supplements. We report for each measure m the posterior distribution of 1 − exp(θ m ), that is, the relative reduction in new cases. 9 All rights reserved. No reuse allowed without permission.

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