Selected article for: "model time series and time integration"
Author: Jonas Dehning; Johannes Zierenberg; Frank Paul Spitzner; Michael Wibral; Joao Pinheiro Neto; Michael Wilczek; Viola Priesemann
Title: Inferring COVID-19 spreading rates and potential change points for case number forecasts
  • Document date: 2020_4_6
    • ID: c8zfz8qt_60
    Snippet: Choose random initial parameters and evolve according to model equations. Initially, we choose parameters θ from prior distributions that we explicitly specify below. Then, time integration of the model equations generates a (fully deterministic) time series of new infected cases C(θ) = {C t (θ)} of the same length as the observed real-world datâ C = Ĉ t ......
    Document: Choose random initial parameters and evolve according to model equations. Initially, we choose parameters θ from prior distributions that we explicitly specify below. Then, time integration of the model equations generates a (fully deterministic) time series of new infected cases C(θ) = {C t (θ)} of the same length as the observed real-world datâ C = Ĉ t .

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