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(B|A), 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(A|B) = ( p(B|A) * p(A) ) / p(B)`

Where A and B are events. p(A) and p(B) are the probabilities of those events.
p(B|A) is the probability of seeing B if A is true. p(B) can be calculated
as `p(B|A) * p(A) + p(B|¬A) *
p(¬A)` where
¬ denotes NOT. e.g.
p(¬A) = 1 - p(A)

Also:

- Machine Learning Method, Bayesian Classification
- Troubleshooting Machine Learning Methods
- Localization (computing position over time from sensor readings)

file: /Techref/method/ai/bayesrule.htm, 2KB, , updated: 2017/4/19 12:58, local time: 2024/11/7 14:26,
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