Sentence examples for model discrepancies are from inspiring English sources

Our findings that allometric model discrepancies are not explained by lidar heights suggests that allometric model form does not drive these discrepancies.

This is not a strength of standard approaches to the statistical analysis of computer models where a certain "best input" assumption is usually made and model discrepancy is often described through a stationary Gaussian process prior on the discrepancy function.

For this simple situation, it is straight-forward to assess changes to the implausibility measure if the observational uncertainty or model discrepancy is reduced.

This model-to-model discrepancy is partially ameliorated by supplying the puff model with more detailed information about the urban boundary layer that evolves on the CFD grid.

Agreement between observational data and the simple model in suitable parameter ranges, allows determination of the relevant parameters characterizing the model, while discrepancies are used to understand the geometry of the scatterers, as they correspond to shape effects (i.e. deviations from the shape considered in the simple model).

The observed model-data discrepancies are primarily due to model inadequacy, such as our simplified modeling of the bulk soil electrical conductivity profile.

The linear model is qualitatively compared to the original model and the discrepancies are quantified in terms of uncertainty weights and included in the control design process.

Proponents of the classical model maintain that these discrepancies are due to the effects of mantle circulation as the plumes ascend, a process called the mantle wind.

To conclude, the main system dynamics are well captured by the model and the minor discrepancies are easily justified by the simplifying assumptions, often leading to optimistic predictions.

Additional putative functional PPO gene models with discrepancies were identified for some genomes, but were excluded from this.

These analyses demonstrate that the normalized-Hill model largely captures key features of the more detailed biochemical model, that specific model discrepancies can be quantitatively explained by parameter differences, and that dynamic predictions can be refined by fitting relevant parameters to available data.