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A weighted, thresholded linear model class was used to combine the motif scanning feature with zero, one or two additional features to predict binding for the five TFs.
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Even cars, within a given model class, are more difficult than ever to tell apart.
As most existing ANN design techniques require the ANN model class to be known before the training process, a technique that can automatically select an "optimal" ANN model class is essential.
A potentially limiting underlying assumption of this model class is that the explanatory variables have a proportional effect on the hazard function.
This model class is defined by a system of partial differential equations (PDEs) for an arbitrary number of parallel compartments with individual transport velocities and numerous lateral exchange connections.
The prior probability of each model class is set to 1 / μ.
The most probable model class is selected as the one that maximizes Pr (M i | D ) over i.
The model class is implemented in a visualization tool that allows variation of the model parameters and visualization of the resulting model for the given set of parameters.
In the field test verification, the posterior marginal PDFs conditional on two model classes are obtained by the proposed MCMC algorithm, which provide valuable information about the identifiability of different model parameters.
We perform online predictions where we fit the data up to step n, and then predict the next location at step (n+1) using a multinomial logistic regression model; classes are simply the set of all possible places visited so far.
Some matchings between unrelated model classes were found.
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CEO of Professional Science Editing for Scientists @ prosciediting.com