Bayesian learning is a machine learning approach that uses probability to represent uncertainty and update beliefs as new evidence becomes available. Instead of treating a model’s parameters or predictions as fixed values, it maintains probability distributions and revises them when additional data is observed.
The process typically begins with a prior belief, incorporates new observations through a likelihood, and produces an updated posterior belief using Bayes’ theorem. As more evidence becomes available, the model can continuously refine its understanding while retaining information from previous observations.
Bayesian learning is useful when uncertainty matters, data is limited, or decisions need to account for changing evidence. Applications include anomaly detection, predictive maintenance, medical diagnosis, forecasting, risk assessment, and adaptive decision-making.
