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Subsequently, we delve into the realm of probability theory and its applications, particularly focusing on conditional probability and Bayes' Theorem. Given two events A and B, conditional probability measures the likelihood of event A occurring given that event B has already occurred. Bayes' Theorem provides a mathematical framework to update the probability of a hypothesis (A) based on new evidence (B). It is expressed as P(A|B) = [P(B|A)
P(A)] / P(B), where P(A|B) is the posterior probability of A given B, P(B|A) is the likelihood of B given A, P(A) is the prior probability of A, and P(B) is the marginal likelihood of B. This theorem is crucial in fields like statistics, machine learning, and decision-making under uncertainty, allowing for the refinement of hypotheses based on observed data.