Bayesian Probability is a statistical method that updates the probability of a hypothesis as more evidence or information becomes available. It is based on Bayes' Theorem, which relates the conditional and marginal probabilities of random events. This approach allows for a more flexible understanding of uncertainty, as it incorporates prior knowledge along with new data. In Bayesian Probability, the initial belief about a situation is called the prior probability. As new evidence is gathered, this prior is adjusted to form the posterior probability, reflecting a more accurate understanding of the situation. This iterative process is useful in various fields, including machine learning and medical diagnosis.