Selected article for: "parameter uncertainty quantify and time series"
Author: Chowell, Gerardo
Title: Fitting dynamic models to epidemic outbreaks with quantified uncertainty: A primer for parameter uncertainty, identifiability, and forecasts
  • Document date: 2017_8_12
    • ID: 3aa8wgr0_65
    Snippet: In the previous section we assumed a Poisson error structure to quantify parameter uncertainty. The Poisson distribution only requires one parameter and is suitable to model count data where the mean of the distribution equals the variance. In situations where the time series data shows overdispersion, we can employ a negative binomial distribution instead. The negative binomial distribution requires two parameters to model the mean and overdispe.....
    Document: In the previous section we assumed a Poisson error structure to quantify parameter uncertainty. The Poisson distribution only requires one parameter and is suitable to model count data where the mean of the distribution equals the variance. In situations where the time series data shows overdispersion, we can employ a negative binomial distribution instead. The negative binomial distribution requires two parameters to model the mean and overdispersion in the data. Thus, it is possible to model variance levels in the data that are relatively higher than the mean.

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