In a previous module, we covered Bayes' theorem and the Bayesian paradigm. Conditional probabilities are a fundamental part of this previous covered rule P (A/B)= P(B/A) P(A) / P(B)
We first review a simple example to go over conditional probabilities.
Assume a patient comes into the doctor's office to test whether they have a particular disease.
The test is positive 85% of the time when tested on a patient with the disease (high sensitivity): P (test+/disease)-0.85 .
The test is negative 90% of the time when tested on a healthy patient (high specificity): P (test-heathy)-0.90 .
The disease is prevalent in about 2% of the community: P (disease)-0.02 Using Bayes' theorem, calculate the probability that you have the disease if the test is positive.