The probability that belief A is true, given new evidence B is equal to the probability of B given A times the probability of A (regardless of B) divided by the probability of B (regardless of A).
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In Bayes’ Theorem, the probability of something occurring is based on probabilities of other parameters of the problem. Put simply, using the theorem builds on prior knowledge of the problem domain to update a prediction. This became very popular because, in the real world, there is much uncertainty and Bayes Theorem provides a way of modeling that uncertainty through probability (e.g. machine learning).