Bayes' theorem in machine learning

Bayes' Theorem describes how to update the probability of a hypothesis when new data is obtained. It reverses conditional probability: instead of asking “what is the probability of the data given the hypothesis?”, we ask “what is the probability of the hypothesis given the observed data?” P(A∣B) = P(B∣A)*P(A) / P(B) P(A∣B) = Likelihood × Prior / Marginal likelihood P(A∣B) — posterior probability. The probability of hypothesis A after observing data B. This is what we want to find. P(B∣A) — likel