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Workload modelling provides a way to bridge this gap.
Fuzzy system modelling provides a strict mathematical environment in which vague conceptual phenomena can be rigorously studied.
Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience.
This modelling provides a good prediction the σyi dependence on Lc and ST in the context of thermally activated rate processes.
Neural network modelling provides a surprisingly coherent perspective of the psychobiology of OCD, simulating both reduced cognitive disinhibition as well as neurotransmitter dysfunction.
Theoretical modelling provides a very powerful method for optimizing the design of such substrates, i.e. tailoring their geometrical parameters for optimizing the optical response.
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The Von Neumann model provides a universal abstraction for sequential computation.
The FE model provides a useful check.
We conclude that such a model provides a potential explanation of the observations.
The tagged signal model provides a denotational framework to study properties of various models of computation.
Moreover, a probabilistic model provides a statistical framework for inference.
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