Bayesian updating starts with a prior distribution that represents uncertainty before observing new data. Bayes' rule combines that prior with the likelihood of the observed evidence to produce a posterior distribution, which becomes the updated account of what is plausible.
The method supports continuous learning from evidence without pretending that one observation creates certainty. Results still depend on the prior, the likelihood model, and data quality. In practice, teams should examine sensitivity to those choices and test whether repeated probability estimates are calibrated.
