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Conditional probability refers to the likelihood of an event occurring given that another event has already occurred. It is denoted as P(B|A), which reads as 'the probability of B given A.' This concept is fundamental in statistics and probability theory, as it helps in understanding how the occurrence of one event can influence the probability of another.

In the notation P(B|A), A and B represent two distinct events within a probability space. Event A is the condition or the event that has already occurred, while event B is the event whose probability we are trying to determine under the condition of A. Understanding the relationship between these events is crucial for calculating conditional probabilities accurately.

Bayes' Theorem is a fundamental principle in probability that relates conditional probabilities of events. It provides a way to update the probability of an event based on new evidence or information. The theorem is often expressed as P(A|B) = [P(B|A) * P(A)] / P(B), illustrating how P(B|A) can be used to infer the probability of A given B, thereby highlighting the interconnectedness of events in probability.