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Spatial modeling is typically composed of a specification of a mean function and a model for the correlation structure.
The signal used for kinetic modeling is typically obtained by defining regions of interest (ROIs), in which the radioactivity concentrations are measured over time.
Training an RNN for sequence modeling is typically done by maximum likelihood estimation of the next token (x^{t}) in the target sequence given tokens for the previous steps (Fig. 1).
The loosely coupled relationships between visualization and analytical data mining (DM) techniques represent the majority of the current state of art in visual data mining; DM modeling is typically an automatic process with very limited forms of guidance from users.
The intended purpose of such expert modeling is typically to demonstrate a good strategy for solving the problem.
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For security software, that model is typically seen as advantageous, because the more people examine a line of code, the more chance there should be of some weakness coming to light.
This estimation model is typically applied in AWGN channels.
This model is typically exemplified by the USA.
This model is typically referred to as the classical Pareto model.
For dynamic stochastic scheduling problems, DES (as discussed in"Evaluation models") is typically applied.
In the case of commercial software, the full documentation of each model is typically not available.
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