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Bayes' theorem is a mathematical formula used to calculate conditional probabilities.

Bayes' theorem is named after Reverend Thomas Bayes, an 18th-century statistician and theologian. It is a fundamental concept in probability theory and is used to calculate conditional probabilities. Conditional probability is the probability of an event occurring given that another event has already occurred. Bayes' theorem is used to update the probability of an event occurring based on new information.

The formula for Bayes' theorem is as follows:

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

where P(A|B) is the probability of event A occurring given that event B has occurred, P(B|A) is the probability of event B occurring given that event A has occurred, P(A) is the prior probability of event A occurring, and P(B) is the prior probability of event B occurring.

Bayes' theorem is used in a wide range of applications, including medical diagnosis, spam filtering, and machine learning. It is a powerful tool for updating probabilities based on new information and can be used to make more accurate predictions and decisions.

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