Team:Arizona State/HPModeling

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We can use Bayesian techniques to estimate natural frequency of the quantity to be measured (pathogen presence) as well as to analyze the accuracy and reliability of our device.
We can use Bayesian techniques to estimate natural frequency of the quantity to be measured (pathogen presence) as well as to analyze the accuracy and reliability of our device.
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Baye's rule gives us the probability of an actual positive event given that our sensor outputs "true". To evaluate this probability we need three pieces of data: specificity ('''A'''), sensitivity ('''B'''), and the "natural frequency" ('''C''') of the event. Sensitivity and specificity are evaluated based on experimental results and are defined below. Estimation of the "natural frequency" of a disease vector is more complex, and can be handled using a Bayesian network or other sophisticated statistical devices. This Bayesian network should be constructed using data from studies such as those referenced in [[Escherichia Coli Case Studies]].
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Baye's rule gives us the probability of an actual positive event given that our sensor outputs "true". To evaluate this probability we need three pieces of data: specificity ('''A'''), sensitivity ('''B'''), and the "natural frequency" ('''C''') of the event. Sensitivity and specificity are evaluated based on experimental results and are defined below. Estimation of the "natural frequency" of a disease vector is more complex, and can be handled using a Bayesian network or other sophisticated statistical devices. This Bayesian network should be constructed using data from studies such as those referenced in [http://2012.igem.org/Team:Arizona_State/FieldApplications <i>Escherichia Coli</i> Case Studies].
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Revision as of 03:44, 4 October 2012

We can use Bayesian techniques to estimate natural frequency of the quantity to be measured (pathogen presence) as well as to analyze the accuracy and reliability of our device.

Baye's rule gives us the probability of an actual positive event given that our sensor outputs "true". To evaluate this probability we need three pieces of data: specificity (A), sensitivity (B), and the "natural frequency" (C) of the event. Sensitivity and specificity are evaluated based on experimental results and are defined below. Estimation of the "natural frequency" of a disease vector is more complex, and can be handled using a Bayesian network or other sophisticated statistical devices. This Bayesian network should be constructed using data from studies such as those referenced in Escherichia Coli Case Studies.


Condition
Condition Positive Condition Negative
Sensor
Outcome
Sensor
Outcome
Positive
True Positive False Positive Positive predictability =
TP
TP + FP
Sensor
Outcome
Negative
False Negative True Negative Negative predictability =
TN
TN + FN
Sensitivity =
TP
TP + TN
Specificity =
TN
TN + FP
  • Sensitivity: proportion of true positives accurately measured
  • Specificity: proportion of true negatives accurately measured
  • Positive predictability: proportion of positive sensor results that are true positives
  • Negative predictability: proportion of negative sensor results that are true negatives

Once we have experimental data giving us values for the table above, we can use Bayes' theorem to estimate the probability of an actual event given a positive sensor reading. To aid in these calculations we have attached an excel spreadsheet: Bayes' rule.