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Bayes’s theorem is a way of finding a probability when we have certain other probabilities. Bayes’s theorem is stated mathematically as the following equation: Where A and B are events and P(B) ≠ 0, P(A | B) is also a conditional probability: the likelihood of event A occurring given  B is true. P(B | A) is also a conditional probability: the likelihood of event B occurring given  A is true. P(A)  and P(B) are conditional probabilities…

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Suppose two events A and B are mutually inclusive events ( having one or more outcomes in common ). Conditional probability i.e. P( A | B ) is the probability of event A occurring with some relationship to event B. Mathematically, P(A | B) = P(A and B) / P(A) which you can also rewrite as: P(A | B) = P(A∩B) / P(A) where P(A) ≠ 0 Conditional Probability in Real World Suppose the weather forecasts…

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