Is a critical tool for avoiding errors while working with skewed data. Assuming an event A, such as a percentage of patients with cancer, or the number of actual drug users in a population. And an event B which is a positive test result for cancer, drug use, whatever... If the test is positive when the actual event has occurred some percentage of the time p(BA), and negative when it should be by some percentage p(¬B¬A) aka the opposite of it being positive when it should not be p(B¬A), Bayes Rule tells us what the actual chance that the event A has happened give a positive test result B.
p(AB) = ( p(BA) * p(A) ) / p(B)
Where A and B are events. p(A) and p(B) are the probabilities of those events. p(BA) is the probability of seeing B if A is true. p(B) can be calculated as p(BA) * p(A) + p(B¬A) * p(¬A) where ¬ denotes NOT. e.g. p(¬A) = 1  p(A)
Also:
See also:
file: /Techref/method/ai/bayesrule.htm, 2KB, , updated: 2017/4/19 13:58, local time: 2018/6/23 11:00,

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