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Models can always be made more detailed or accurate but that ultimately means relying on bigger, faster computers to run the simulation.
These kinds of limitations apply more generally: since models can always be improved, statistics tends to only provide lower bounds on the uncertainty of inferences about complex systems (cf. Cox, 2001).
A recent study has shown that such models can always be improved upon with amino-acid dependent transition rates, even if amino acids are grouped into exchangeability categories at random [ 35].
Data-driven models can always be built given an appropriate data set; the problem lies in the fact that there are typically hundreds to thousands of possible models for a given data set and so model selection techniques must be employed.
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But an artist and model can always be ferreted out, though which is which often remains ambiguous.
The mode-based meta-model can always be advantageously employed when a limited number of modes are required for analysis, such as, e.g. for mode superposition analysis.
We also show how the knowledge embedded in an RDP neural network model can always be expressed, transparently, as a finite union of open polytopes.
It is well known that an infinite order AR model can always model the signal arbitrarily closely.
The minimum number of stages in the cross-layer EE model can always be shorter than the minimum number of stages in the Goodman EE formulation.
Show that the following aspects of the cross-layer model improve considerably the system performances when comparing to the Goodman model even for large number of users: The minimum number of stages in the cross-layer EE model can always be shorter than the minimum number of stages in the Goodman EE formulation.
The reduced model can always be represented graphically as a network of states, and lumped variables, where each state of the original model has a clear interpretation as a fraction of one lumped state (or possibly left unchanged).
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