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The best steamers are heavy-duty models with a large plate, ideally at least one foot square.
PEPS can solve complex models with a large state space and with many synchronized events.
The multidisciplinary nature of most design problems can generate complicated models with a large set of variables and equations involved.
Therefore in particular for process development reliable predictive models with a large range of applicability are most important.
These approaches often yield models with a large model uncertainty, especially when the observed data are scattered.
Artificial neural networks (ANN) modeling suffers from difficulties in training models with a large number of inputs.
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We look forward to testing the adaptations further as we implement the revised PSC/TLP models with a larger group of schools in our phase two comparison study.
These statistics account for both model fit (deviance) and model complexity by penalizing models with a larger number of parameters.
The statistics BIC and AIC were also calculated, in order to penalize models with a larger number of parameters.
In future studies, we intend to determine the performance of our statistical models with a larger set of cancer patients, in which CIMP status is defined.
It is important to confirm the differences in metabolic lab values between progressors and non-progressors and to validate the classification models with a larger set of patients.
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