Bayes’ Theorem is used in probability to update our beliefs about the probability of an event occurring based on new information or evidence that becomes available. It can be used to calculate the conditional probability of an event given some other event, by using prior probabilities and the likelihood of the evidence.

Here is the general formula for Bayes’ Theorem in probability:

P(A | B) = P(B | A) * P(A) / P(B)

where:

- P(A | B) is the probability of event A given that event B has occurred.
- P(B | A) is the probability of event B given that event A has occurred.
- P(A) is the prior probability of event A.
- P(B) is the overall probability of event B.

To use Bayes’ Theorem in probability, we first need to know the prior probability of the event we are interested in (P(A)), as well as the likelihood of the evidence (P(B | A)) and the overall probability of the evidence (P(B)). Once we have these values, we can plug them into the formula to calculate the conditional probability of the event we are interested in (P(A | B)).

For example, suppose we want to calculate the probability of a person having a certain disease (event A) given that they have tested positive for the disease (event B). Let’s say that the prior probability of the person having the disease is 0.01 (1%), the probability of a positive test given that the person has the disease is 0.99 (99%), and the probability of a positive test given that the person does not have the disease is 0.05 (5%). Using Bayes’ Theorem, we can calculate the probability of the person having the disease given a positive test result as follows:

P(A | B) = P(B | A) * P(A) / P(B) P(A | B) = 0.99 * 0.01 / ((0.99 * 0.01) + (0.05 * 0.99)) P(A | B) = 0.165 = 16.5%

So in this case, the probability of the person having the disease given a positive test result is 16.5%.