However, a validation on the basis of insurance claims might be more conservative in cases where model errors are more pronounced in areas with a high density of values at risk.
Finally, in all cases, assumptions about model error were verified using standard procedures for time series analysis.
With reference to a simple case-study, the effect of model uncertainties on results is described and the reliability and the robustness of the proposed correlation function in the case of model errors are tested with reference to 2D and 3D benchmark case studies.
First, a closed form solution of the optimal noncausal filter, which minimizes the worst-case model matching error for each ω∈[0,2π], is obtained.
In order to tackle the identifiability a priori, we consider the error free case where neither experimental nor model error is assumed, and explore the parameter space in order to find the viable parameter regions i.e. subsets of the parameter space where the reaction network model maintains the desired behaviour.
Numerical experiments highlight the advantages of the proposed methods especially JD LU +, in the case of dealing with high-variance model error and with degeneracies such as bottlenecks.
For dense tropical forests on steep terrain, variability in sampling density and footprint characteristics can introduce large biases in lidar-based estimates of aboveground biomass (up to 80 125 Mg ha−1 error in estimated biomass vs. 30 Mg ha−1 model error in our case), based on the underestimation of canopy height in areas with low sampling density.
In a case study on forest soil eutrophication, inclusion of the model error led to a considerable increase of the error variance for most variables.
FiveThirtyEight's forecasting model, like any other forecasting model, is subject to at least two sources of error: sampling error and model error.
Model error covariance matrix.
