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In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule) describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

wiki gives this example to illustrate Bayes' theorem

Suppose that a test for using a particular drug is 99% sensitive and 99% specific. That is, the test will produce 99% true positive results for drug users and 99% true negative results for non-drug users. Suppose that 0.5% of people are users of the drug. What is the probability that a randomly selected individual with a positive test is a drug user?

${\displaystyle {\begin{aligned}P({\text{User}}\mid {\text{+}})&={\frac {P({\text{+}}\mid {\text{User}})P({\text{User}})}{P(+)}}\\&={\frac {P({\text{+}}\mid {\text{User}})P({\text{User}})}{P({\text{+}}\mid {\text{User}})P({\text{User}})+P({\text{+}}\mid {\text{Non-user}})P({\text{Non-user}})}}\\[8pt]&={\frac {0.99\times 0.005}{0.99\times 0.005+0.01\times 0.995}}\\[8pt]&\approx 33.2\%\end{aligned}}}$

to make this discussion more concrete, assume there are 10,000,000 people in the computation.

0.5% of people are users of the drug means there are 50,000 people are drug users.

$P({\text{+}}\mid {\text{User}})$ represents that the test will produce 99% true positive results for the 50,000 drug users, that is, 4,950 people

in this context, what does "$P(\text{+})$" mean?

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$P(+)$ represent the probability that the test will produce positive result.

This is computed using law of total probability in the denominator.

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