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The method directly models a predictive distribution of terms conditioning on training data with a Gaussian process (GP) prior.
This work is a quintessential example of machine learning in encoding models: a predictive engine performs the same task as the brain system under study; machine learning is used to fit its parameters on a set of stimuli and the final architecture matches neural data.
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Using the characteristic structure of the nonlinear model, a predictive feedback controller based on a gain schedule technique is incorporated in the design to achieve the performance objectives.
Incorporating outputs of the probit model, a predictive equation is presented that can identify under what level a segment is offering services for bicycle use.
This model is a predictive model.
A General Predictive Model is a predictive model established considering data collected from projects of several organizations.
Using Bayesian model selection, a predictive model for total soil carbon was developed.
Recall that both approaches produced the same model - a predictive model consisting of age, baseline WBC, T-cell immunophenotype and splenomegaly.
However successful application of models as a predictive tool requires that they be experimentally calibrated.
We gave our CPG models such a predictive task in a simulated environment (Figure 4A).
By combining the two, the researchers were able to produce a model with a predictive power of 61.8%.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com