Calculates a posterior probability based on the prior conditional probabilities.
Bayes' rule can be described as a way to improve a prior belief by incorporating observed data, related to this belief (like test data or sensor measurements). The rule is written as:
P(A|B) = P(B|A) * P(A) / P(B)
Where A is the event and B is some observed, related data.
Given only three probabilities: p_A, p_B_given_A, and p_notB_given_notA, which can be written in notation as:
P(A), P(B|A) P(notB|notA)
This function calculates the posterior probability: P(A|B)
To use bayes.py, install this code base and run the following command:
python bayes.py --pA <float> --pBgivenA <float> --pnotBgivennotA <float>