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These studies suggest that parameter uncertainty and initial conditions uncertainty can both affect model outcomes, but their relative impact varies depending on the context and adjustments made.
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Understanding the interplay between parameter uncertainty and initial conditions uncertainty is crucial for improving the accuracy and reliability of model predictions. This synthesis explores how these two types of uncertainties affect each other and the overall model performance.
Impact on Model Variance:
Non-linear Effects:
Initial Condition Uncertainty:
Relative Importance:
Parameter uncertainty and initial conditions uncertainty both significantly impact model predictions, but their relative importance varies with context. Parameter uncertainty generally increases model variance, especially in non-linear models. Initial condition uncertainty is critical in the short term but diminishes over longer periods. In specific scenarios, such as climate-sensitive disease modeling, the uncertainty in driving data can be more influential than parameter uncertainty.
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