The results are shown in Figure 6, and the exact model assumptions are described in the figure legend.
Testing how methods that infer speciation and extinction rates perform when their underlying model assumptions are violated.
Parametric methods are commonly used despite evidence that model assumptions are often violated.
The model assumptions are discussed extensively as the equations are presented.
If the residual analysis does not indicate that the model assumptions are satisfied, it often suggests ways in which the model can be modified to obtain better results.
Some of the main model assumptions are highlighted, and their implications for practical model application are discussed.
Since underlying model assumptions are encoded in the manifold, PEC restricts the application of submodels within its intended use.
In particular, why has the HMM, whose very strong model assumptions are clearly violated by speech data, been so hard to remove from the core of nearly all speech recognition systems?
Discrepancies between the numerical and experimental results of the bridge piers response are highlighted and deficiencies in the numerical model assumptions are discussed.
However, these estimation methods cause loss of information through the introduction of biases in each filtering step, which are aggregated throughout the process when model assumptions are violated, and the traditional BPNN model does not have forecasting ability.
These modelling methodologies thus complement the process knowledge included in white-box models with predictions based on data in areas where the white-box model assumptions are not valid or where white-box models do not provide accurate predictions.
