Whether well-known or obscure, each of the nearly 170 women photographed will be looked at (especially by other women) as models: models of beauty, models of self-esteem, models of strength, models of transgressiveness, models of victimhood, models of false consciousness, models of successful ageing.
With auxiliary information associated with each specimen, model estimates of false negative rates based on, for example, test type, time since onset of symptoms, age of the patient, or clinical presentation would have allowed us to explore the reasons for the high false negative rates.
Data obtained with the automated MATLAB script were used to generate accuracy reports for our model consisting of false positives/negatives and true positives/negatives.
From those simulations and the associated experiment, we automatically generated MATLAB tests, which generated an accuracy report of our model, consisting of False Positives/Negatives, True Positives/Negatives between the expected and predicted phenotype.
The models of false-modesty: these elected officials have been lauded as Tea Party leaders despite professed reluctance and unsure attachment.
We note that our particular model of link uncertainty (false negative vs. false positive rates) may be significant here: even for the highest (p_{text {neg}}), our uncertainty model is density preserving: (G') resembles a random graph with each edge being present with probability (p_{text {pos}}).
This assumption allows us to model the rate of false alarms per unit of time and compare this to the rate of true-positive alarms.
However, PM fails to capture, especially for well-performing models, the influence of false positives.
In fact, the standard view in the 1950s and early 1960s was that the singularities of the FRW models were consequences of false idealizing assumptions, namely assumptions of perfect isotropy and homogeneity.
Sensitivity was calculated as the number of influenza positive tests divided by the number of influenza positive tests plus the model-estimated number of false negative tests.
