Bayesian updating is a statistical method used to revise existing beliefs or predictions based on new evidence. It combines prior knowledge, represented as a prior probability, with new data to produce an updated belief, known as the posterior probability. This process allows for continuous learning and adaptation as more information becomes available. The core principle of Bayesian updating is Bayes' theorem, which mathematically describes how to update probabilities. By applying this theorem, individuals and researchers can make more informed decisions in various fields, including medicine, finance, and machine learning, where uncertainty is common and new data frequently emerges.