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_72
Snippet: Since change point detection entails evaluating models with different numbers of parameters, some form of fair model comparison needs to be performed. Here, we compared the models with different numbers of change points by their pointwise out-of-sample prediction accuracy using the log-likelihood evaluated at the posterior simulations of the parameter values obtained from the fitted models. Out-of-sample accuracy was approximated using Leave-one-.....
Document: Since change point detection entails evaluating models with different numbers of parameters, some form of fair model comparison needs to be performed. Here, we compared the models with different numbers of change points by their pointwise out-of-sample prediction accuracy using the log-likelihood evaluated at the posterior simulations of the parameter values obtained from the fitted models. Out-of-sample accuracy was approximated using Leave-one-out cross-validation (LOO) [37] author/funder, who has granted medRxiv a license to display the preprint in perpetuity. author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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